Ulrike Von Luxburg, Aus Tübingen, Von Fakultät, Iv Elektrotechnik Informatik, Vorsitzender Prof, Dr. F. Wysotzki, ...
vorgelegt von
Prototype Classification: Insights from Machine Learning (2009)
Graf, A.B.A., Bousquet, O., Rätsch, G., Schölkopf, B.
We shed light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework. The discrimination process is then...
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance (2009)
Hill, J., Farquhar, J., Martens, S.M.M., Biessmann, F., Schölkopf, B.
From an information-theoretic perspective, a noisy transmission system such as a visual Brain-Computer Interface (BCI) speller could benefit from the use of errorcorrecting codes. However, optimizing...
Denoising photographs using dark frames optimized by quadratic programming (2009)
Gomez Rodriguez, M., Kober, J., Schölkopf, B.
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Online blind deconvolution for Astronomy (2009)
Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B.
Atmospheric turbulences blur astronomical images taken by earth-based telescopes. Taking many short-time exposures in such a situation provides noisy images of the same object, where each noisy image...
Characteristic Kernels on Groups and Semigroups (2009)
Fukumizu, K., Sriperumbudur, B.K., Gretton, A., Schölkopf, B.
Embeddings of random variables in reproducing kernel Hilbert spaces (RKHSs) may be used to conduct statistical inference based on higher order moments. For sufficiently rich (characteristic) RKHSs,...
Nonlinear causal discovery with additive noise models (2009)
Hoyer, P.O., Janzing, D., Mooij, J.M., Peters, J., Schölkopf, B.
The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models are often used because these...
Kernel Methods for Detecting the Direction of Time Series (2009)
Peters, J., Janzing, D., Gretton, A., Schölkopf, B.
We propose two kernel based methods for detecting the time direction in empirical time series. First we apply a Support Vector Machine on the finitedimensional distributions of the time series...
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis (2009)
Schweikert, G., Widmer, C., Schölkopf, B., Rätsch, G.
We study the problem of domain transfer for a supervised classification task in mRNA splicing. We consider a number of recent domain transfer methods from machine learning, including some that are...
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences (2009)
Seeger, M.W., Nickisch, H., Pohmann, R., Schölkopf, B.
We show how improved sequences for magnetic resonance imaging can be found through automated optimization of Bayesian design scores. Combining recent advances in approximate Bayesian inference and...
Diffeomorphic Dimensionality Reduction (2009)
This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data points. We argue that constraining the mapping between the high and low dimensional...
Covariate Shift and Local Learning by Distribution Matching (2009)
Gretton, A., Smola, A.J., Huang, J., Schmittfull, M., Borgwardt, K.M., Schölkopf, B.
Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve this...
zur Erlangung des akademischen Grades Promotionsausschuß: (2008)
Vorgelegt Von, Aus Weimar, Vorsitzender Prof, Dr. H. Ehrig, Berichter Prof, ...
Neurowissenschaft sowie aus der computergestützten Bildverarbeitung stammen. Die dabei zur Lösung von Klassifikationsproblemen eingesetzten Kernalgorithmen erlauben auch die Behandlung komplexer...
Eurographics E. Gröller, L. Szirmay-kalos, C. Walder, B. Schölkopf, O. Chapelle
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The...
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
Self-Organizing Map Algorithm Without Learning of Neighborhood Vectors (2008)
B. Schölkopf, A. J. Smola, Learning Kernels, K. F. Man, K. S. Tang, S. Kwong, ...
search method has been developed based on boosting to append classifier kernels one by one in an orthogonal forward regression procedure. Experimental results presented have demonstrated the...
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, C. Lemmen, A. Smola
Abstract In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding proteins start, the so-called translation initiation...
Contour-Propagation Algorithms for Semi-Automated Reconstruction of Neural Processes (2008)
Macke, J.H., Maack, N., Gupta, R., Denk, W., Schölkopf, B., Borst, A.
A new technique, ”Serial Block Face Scanning Electron Microscopy” (SBFSEM), allows for automatic sectioning and imaging of biological tissue with a scanning electron microscope. Image stacks...
Support vector machines and kernels for computational biology (2008)
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.
Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques (2008)
Hofmann, M., Pichler, B., Schölkopf, B., Beyer, T.
Introduction Positron emission tomography (PET) is a fully quantitative technology for imaging metabolic pathways and dynamic processes in vivo. Attenuation correction of raw PET data is a...
Kernels, Regularization and Differential Equations (2008)
Many common machine learning methods such as Support Vector Machines or Gaussian process inference make use of positive definite kernels, reproducing kernel Hilbert spaces, Gaussian processes, and...
Support Vector Machines and Kernels for Computational Biology (2008)
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.
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Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., ...
For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating...
Similarity, Kernels, and the Triangle Inequality (2008)
Jäkel, F., Schölkopf, B., Wichmann, F.A.
Similarity is used as an explanatory construct throughout psychology and multidimensional scaling (MDS) is the most popular way to assess similarity. In MDS, similarity is intimately connected to the...
Voluntary Brain Regulation and Communication with ECoG-Signals (2008)
Hinterberger, T., Widmann, G., Lal, T.N., Hill, J., Tangermann, M., Rosenstiel, W., ...
Brain–computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the...
Laubinger, S., Zeller, G., Henz, S.R., Sachsenberg, T., Widmer, C.K., Naouar, N., ...
Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas,...
Kernel Methods in Machine Learning (2008)
Hofmann, T., Schölkopf, B., Smola, A.J.
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on...
Jäkel, F., Schölkopf, B., Wichmann, F.A.
Exemplar theories of categorization depend on similarity for explaining subjects’ ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction...
Manifold-valued Thin-plate Splines with Applications in Computer Graphics (2008)
Steinke, F., Hein, M., Peters, J., Schölkopf, B.
We present a generalization of thin-plate splines for interpolation and approximation of manifold-valued data, and demonstrate its usefulness in computer graphics with several applications from...
Protein Functional Class Prediction With a Combined Graph (2008)
Shin, H.H., Tsuda, K., Schölkopf, B.
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as...
Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods (2008)
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to quantify the complexity of conditional probability measures by a Hilbert space seminorm of the logarithm of its density. The concept of reproducing kernel Hilbert spaces...
Contour-propagation Algorithms for Semi-automated Reconstruction of Neural Processes (2008)
Macke, J.H., Maack, M., Gupta, R., Denk, W., Schölkopf, B., Borst, A.
A new technique, ”Serial Block Face Scanning Electron Microscopy” (SBFSEM), allows for automatic sectioning and imaging of biological tissue with a scanning electron microscope. Image stacks...
Automatic Image Colorization Via Multimodal Predictions (2008)
Charpiat, G., Hofmann, M., Schölkopf, B.
We aim to color automatically greyscale images, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic...
Automatic 3D Face Reconstruction from Single Images or Video (2008)
Breuer, P., Kim, K.I., Kienzle, W., Schölkopf, B., Blanz, V.
This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support...
Kernel Measures of Conditional Dependence (2008)
Fukumizu, K., Gretton, A., Sun, X., Schölkopf, B.
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence...
A Kernel Statistical Test of Independence (2008)
Gretton, A., Fukumizu, K., Teo, C.H., Song, L., Schölkopf, B., Smola, A.J.
Whereas kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically...
An Analysis of Inference with the Universum (2008)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B.
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
RKHS Representation of Measures Applied to Homogeneity, Independence, and Fourier Optics (2008)
Schölkopf, B., Sriperumbudur, B.K., Gretton, A., Fukumizu, K.
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Tailoring density estimation via reproducing kernel moment matching (2008)
Song, L., Zhang, X., Smola, A., Gretton, A., Schölkopf, B.
Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distributions into a...
Injective Hilbert Space Embeddings of Probability Measures (2008)
Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Lanckriet, G., Schölkopf, B.
A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing and independence testing. This embedding...
Sparse Multiscale Gaussian Process Regression (2008)
Walder, C., Kim, K.I., Schölkopf, B.
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of...
Learning Inverse Dynamics: A Comparison (2008)
Nguyen-Tuong, D., Peters, J., Seeger, M., Schölkopf, B.
While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexities of manually...
Support Vector Machines and Kernels for Computational Biology (2008)
Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B., Rätsch, G.
Voluntary Brain Regulation and Communication with ECoG-Signals (2008)
Hinterberger, T., Widmann, G., Lal, T.N., Hill, J., Tangermann, M., Rosenstiel, W., ...
Braincomputer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the...
Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques (2008)
Hofmann, M., Pichler, B., Schölkopf, B., Beyer, T.
Introduction Positron emission tomography (PET) is a fully quantitative technology for imaging metabolic pathways and dynamic processes in vivo. Attenuation correction of raw PET data is a...
Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., ...
For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating...
Kernel Methods in Machine Learning (2008)
Hofmann, T., Schölkopf, B., Smola, A.J.
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on...
Jäkel, F., Schölkopf, B., Wichmann, F.A.
Exemplar theories of categorization depend on similarity for explaining subjects ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction...
Similarity, Kernels, and the Triangle Inequality (2008)
Jäkel, F., Schölkopf, B., Wichmann, F.A.
Similarity is used as an explanatory construct throughout psychology and multidimensional scaling (MDS) is the most popular way to assess similarity. In MDS, similarity is intimately connected to the...
Laubinger, S., Zeller, G., Henz, S.R., Sachsenberg, T., Widmer, C.K., Naouar, N., ...
Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas,...
Contour-propagation Algorithms for Semi-automated Reconstruction of Neural Processes (2008)
Macke, J.H., Maack, N., Gupta, R., Denk, W., Schölkopf, B., Borst, A.
A new technique, Serial Block Face Scanning Electron Microscopy (SBFSEM), allows for automatic sectioning and imaging of biological tissue with a scanning electron microscope. Image stacks...
Protein Functional Class Prediction With a Combined Graph (2008)
Shin, H.H., Tsuda, K., Schölkopf, B.
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as...
Kernels, Regularization and Differential Equations (2008)
Many common machine learning methods such as Support Vector Machines or Gaussian process inference make use of positive definite kernels, reproducing kernel Hilbert spaces, Gaussian processes, and...
Manifold-valued Thin-plate Splines with Applications in Computer Graphics (2008)
Steinke, F., Hein, M., Peters, J., Schölkopf, B.
We present a generalization of thin-plate splines for interpolation and approximation of manifold-valued data, and demonstrate its usefulness in computer graphics with several applications from...
Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods (2008)
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to quantify the complexity of conditional probability measures by a Hilbert space seminorm of the logarithm of its density. The concept of reproducing kernel Hilbert spaces...
Automatic 3D Face Reconstruction from Single Images or Video (2008)
Breuer, P., Kim, K.I., Kienzle, W., Schölkopf, B., Blanz, V.
This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support...
Automatic Image Colorization Via Multimodal Predictions (2008)
Charpiat, G., Hofmann, M., Schölkopf, B., Forsyth, D. A., Torr, P. H.S., Zisserman, A.
We aim to color automatically greyscale images, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic...
Kernel Measures of Conditional Dependence (2008)
Fukumizu, K., Gretton, A., Sun, X., Schölkopf, B., Platt, J. C., Koller, D., ...
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence...
A Kernel Statistical Test of Independence (2008)
Gretton, A., Fukumizu, K., Teo, C.H., Song, L., Schölkopf, B., Smola, A.J., ...
Whereas kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically...
Learning Inverse Dynamics: A Comparison (2008)
Nguyen-Tuong, D., Peters, J., Seeger, M., Schölkopf, B., Verleysen, M.
While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexities of manually...
RKHS Representation of Measures Applied to Homogeneity, Independence, and Fourier Optics (2008)
Schölkopf, B., Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Jetter, K., Smale, S., ...
An Analysis of Inference with the Universum (2008)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B., Platt, J. C., Koller, D., ...
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
Tailoring density estimation via reproducing kernel moment matching (2008)
Song, L., Zhang, X., Smola, A., Gretton, A., Schölkopf, B., Cohen, W. W., ...
Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distributions into a...
Injective Hilbert Space Embeddings of Probability Measures (2008)
Sriperumbudur, B.K., Gretton, A., Fukumizu, K., Lanckriet, G., Schölkopf, B., Servedio, R. A., ...
A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing and independence testing. This embedding...
Sparse Multiscale Gaussian Process Regression (2008)
Walder, C., Kim, K.I., Schölkopf, B., Cohen, W. W., McCallum, A., Roweis, S. T.
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of...
Improving the Caenorhabditis elegans genome annotation using machine learning (2007)
Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., ...
For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and...
Predicting Structured Data (2007)
BakIr, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional...
Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference (2007)
Schölkopf, B., Platt, J., Hofmann, T.
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees--physicists,...
Jäkel, F., Schölkopf, B., Wichmann, F.A.
Exemplar theories of categorization depend on similarity for explaining subjects’ ability to generalize to new stimuli. A major criticism of exemplar theories concerns their lack of abstraction...
Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA (2007)
Waldert, S., Bensch, M., Bogdan, M., Rosenstiel, W., Schölkopf, B., Lowery, C.L., ...
Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the...
A Tutorial on Kernel Methods for Categorization (2007)
Jäkel, F., Schölkopf, B., Wichmann, F.A.
The abilities to learn and to categorize are fundamental for cognitive 8 systems, be it animals or machines, and therefore have attracted attention 9 from engineers and psychologists alike. Modern...
Feature Selection for trouble shooting in complex assembly lines (2007)
Pfingsten, T., Herrmann, D.J.L., Schnitzler, T., Feustel, A., Schölkopf, B.
The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products’ integrity can often only be checked in a final...
MR-Based PET Attenuation Correction: Method and Validation (2007)
Hofmann, M., Steinke, F., Scheel, V., Charpiat, C., Brady, M., Schölkopf, B., ...
PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of...
A kernel-based causal learning algorithm (2007)
Sun, X., Janzing, D., Schölkopf, B.
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the...
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities....
Transductive Classification via Local Learning Regularization (2007)
The idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive...
On the Pre-Image Problem in Kernel Methods (2007)
BakIr, G.H., Schölkopf, B., Weston, J.
In this chapter we are concerned with the problem of reconstructing patterns from their representation in feature space, known as the pre-image problem. We review existing algorithms and propose a...
An Analysis of Inference with the Universum (2007)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B.
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
The need for open source software in machine learning (2007)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
Common Sequence Polymorphisms Shaping Genetic Diversity in Arabidopsis thaliana (2007)
Clark, R.M., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., ...
The genomes of individuals from the same species vary in sequence as a result of different evolutionary processes. To examine the patterns of, and the forces shaping, sequence variation in...
Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA (2007)
Waldert, S., Bensch, M., Bogdan, M., Rosenstiel, W., Schölkopf, B., Lowery, C.L., ...
Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the...
Predicting Structured Data (2007)
BakIr, G.H., Hofmann, T., Schölkopf, B., Smola, A.J., Taskar, B., Vishwanathan, S.V.N.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional...
The Need for Open Source Software in Machine Learning (2007)
Sonnenburg, S., Braun, M.L., Ong, C.S., Bengio, S., Bottou, L., Holmes, G., ...
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a...
A Tutorial on Kernel Methods for Categorization (2007)
Jäkel, F., Schölkopf, B., Wichmann, F.A.
The abilities to learn and to categorize are fundamental for cognitive 8 systems, be it animals or machines, and therefore have attracted attention 9 from engineers and psychologists alike. Modern...
Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference (2007)
Schölkopf, B., Platt, J., Hofmann, T.
he annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees--physicists,...
Feature Selection for trouble shooting in complex assembly lines (2007)
Pfingsten, T., Herrmann, D.J.L., Schnitzler, T., Feustel, A., Schölkopf, B.
The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products integrity can often only be checked in a final...
Learning with Hypergraphs: Clustering, Classification, and Embedding (2007)
Zhou, D., Huang, J., Schölkopf, B.
We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are...
Local Learning Projections (2007)
Wu, M., Yu, K., Yu, S., Schölkopf, B.
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the...
A Local Learning Approach for Clustering (2007)
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on...
Transductive Classification via Local Learning Regularization (2007)
The idea of local learning, classifying a particular point based on its neighbors, has been successfully applied to supervised learning problems. In this paper, we adapt it for Transductive...
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions (2007)
Walder, C., Schölkopf, B., Chapelle, O.
We consider the problem of constructing a function whose zero set is to represent a surface, given sample points with surface normal vectors. The contributions include a novel means of regularising...
A kernel-based causal learning algorithm (2007)
Sun, X., Janzing, D., Schölkopf, B., Fukumizu, K.
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the...
A Hilbert Space Embedding for Distributions (2007)
Smola, A., Gretton, A., Song, L., Schölkopf, B., Hutter, M., Servedio, R. A., ...
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert...
Towards Machine Learning of Motor Skills (2007)
Peters, J., Schaal, S., Schölkopf, B.
Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal during the heydays of...
A Nonparametric Approach to Bottom-Up Visual Saliency (2007)
Kienzle, W., Wichmann, F.A., Schölkopf, B., Franz, M.O.
This paper addresses the bottom-up influence of local image information on human eye movements. Most existing computational models use a set of biologically plausible linear filters, e.g., Gabor or...
Kienzle, W., Schölkopf, B., Wichmann, F., Franz, M.O.
Interest point detection in still images is a well-studied topic in computer vision. In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this...
Correcting Sample Selection Bias by Unlabeled Data (2007)
Huang, J., Smola, A., Gretton, A., Borgwardt, K.M., Schölkopf, B.
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover...
Sun, X., Janzing, D., Schölkopf, B.
We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities....
Learning Dense 3D Correspondence (2007)
Steinke, F., Schölkopf, B., Blanz, V.
Establishing correspondence between distinct objects is an important and nontrivial task: correctness of the correspondence hinges on properties which are difficult to capture in an a priori...
A Kernel Method for the Two-Sample-Problem (2007)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a...
A Kernel Approach to Comparing Distributions (2007)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert...
Brain Computer Interfaces for Communication in Paralysis: a Clinical-Experimental Approach (2007)
Hinterberger, T., Nijboer, F., Kübler, A., Matuz, T., Furdea, A., Mochty, U., ...
An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental...
Automatic 3D Face Reconstruction from Single Images or Video (2007)
P. Breuer, K. I. Kim, W. Kienzle, V. Blanz, B. Schölkopf, P. Breuer, ...
Abstract. This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of...
Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA (2007)
Waldert, S., Bensch, M., Bogdan, M., Rosenstiel, W., Schölkopf, B., Lowery, C.L., ...
Classifying event-related desynchronization in EEG, ECoG and MEG signals (2006)
Hill, J.N., Navin Lal, T., Schröder, M., Hinterberger, T., Widman, G., Elger, C.E., ...
Combining a Filter Method with SVMs (2006)
Lal, T.N., Chapelle, O., Schölkopf, B., Guyon, I., Gunn, S., Nikravesh, M., ...
Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature...
A Local Learning Approach for Clustering (2006)
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on...
A Nonparametric Approach to Bottom-Up Visual Saliency (2006)
Kienzle, W., Wichmann, F.A., Schölkopf, B., Franz, M.O.
This paper addresses the bottom-up influence of local image information on human eye movements. Most existing computational models use a set of biologically plausible linear filters, e.g., Gabor or...
Learning an Interest Operator from Human Eye Movements (2006)
Kienzle, W., Wichmann, F.A., Schölkopf, B., Franz, M.O.
We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine...
Time-Dependent Demixing of Task-Relevant EEG Signals (2006)
Hill, N.J., Farquhar, J., Lal, T.N., Schölkopf, B.
Given a spatial filtering algorithm that has allowed us to identify task-relevant EEG sources, we present a simple approach for monitoring the activity of these sources while remaining relatively...
Regularised CSP for Sensor Selection in BCI (2006)
Farquhar, J., Hill, N.J., Lal, T.N., Schölkopf, B.
The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification...
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support (2006)
Walder, C., Schölkopf, B., Chapelle, O.
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The...
Large Scale Multiple Kernel Learning (2006)
Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of...
A Direct Method for Building Sparse Kernel Learning Algorithms (2006)
Wu, M., Schölkopf, B., Bakir, G.
Many Kernel Learning Algorithms(KLA), including Support Vector Machine (SVM), result in a Kernel Machine (KM), such as a kernel classifier, whose key component is a weight vector in a feature space...
Hill, N.J., Lal, T.N., Schröder, M., Hinterberger, T., Wilhelm, B., Nijboer, F., ...
We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of EEG or ECoG signals for each subject. We...
The effect of artifacts on dependence measurement in fMRI (2006)
Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N.K.
A unifying view of Wiener and Volterra theory and polynomial kernel regression (2006)
Volterra and Wiener series are perhaps the best understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their...
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006)
Borgwardt, K., Gretton, A., Rasch, M., Schölkopf, B., Smola, A.
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based...
Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference (2006)
Weiss, Y., Schölkopf, B., Platt, J.
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees--physicists, neuroscientists, mathematicians,...
Semi-Supervised Learning (2006)
Chapelle, O., Schölkopf, B., Zien, A.
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in...
Evaluating predictive uncertainty challenge (2006)
Quinonero-Candela, J., Rasmussen, C.E., Sinz, F., Bousquet, O., Schölkopf, B.
This Chapter presents the PASCAL(1) Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provides a discussion...
Large scale multiple kernel learning (2006)
Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of...
A tutorial on energy-based learning (2006)
Yann Lecun, Sumit Chopra, Raia Hadsell, Fu Jie Huang, G. Bakir, T. Hofman, ...
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables...
A tutorial on energy-based learning (2006)
Yann Lecun, Sumit Chopra, Raia Hadsell, Fu Jie Huang, G. Bakir, T. Hofman, ...
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables...
Object correspondence as a machine learning problem (2005)
Schölkopf, B., Steinke, F., Blanz, V., Raedt, L. De, Wrobel, S.
We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields...
Maximal Margin Classification for Metric Spaces (2005)
Hein, M., Bousquet, O., Schölkopf, B.
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We...
Face Detection - Efficient and Rank Deficient (2005)
Kienzle, W., Bakir, G., Franz, M., Schölkopf, B., Weiss, Y.
This paper proposes a method for computing fast approximations to support vector decision functions in the field of object detection. In the present approach we are building on an existing algorithm...
Methods Towards Invasive Human Brain Computer Interfaces (2005)
Lal, T.N., Hinterberger, T., Widman, G., Schröder, M., Hill, J., Rosenstiel, W., ...
During the last ten years there has been growing interest in the development of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to...
Training Support Vector Machines with Multiple Equality Constraints (2005)
In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization...
Support Vector Machines for 3D Shape Processing (2005)
Steinke, F., Schölkopf, B., Blanz, V.
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the...
Semi-supervised Learning on Directed Graphs (2005)
Zhou, D., Schölkopf, B., Hofmann, T., Saul, L.K., Weiss, Y., Bottou, L.
Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To...
A gene expression map of Arabidopsis thaliana development (2005)
Schmid, M., Davison, T., Henz, S.R., Pape, U.J., Demar, M., Vingron, M., ...
Regulatory regions of plant genes tend to be more compact than those of animal genes, but the complement of transcription factors encoded in plant genomes is as large or larger than that found in...
Weston, J., Schölkopf, B., Bousquet, O., Cabestany, J., Prieto, A., Sandoval, F., ...
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension,...
Implicit Wiener series for higher-order image analysis (2005)
Franz, M.O., Schölkopf, B., Saul, L.K., Weiss, Y., Bottou, L.
The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an...
Machine Learning Applied to Perception: Decision Images for Classification (2005)
Wichmann, F.A., Graf, A.B.A., Simoncelli, E.P., Bülthoff, H.H., Schölkopf, B., Saul, L. K., ...
We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the...
Learning from Labeled and Unlabeled Data on a Directed Graph (2005)
Zhou, D., Huang, J., Schölkopf, B., Raedt, L. De, Wrobel, S.
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time...
Fast Protein Classification with Multiple Networks (2005)
Tsuda, K., Shin, H.H., Schölkopf, B.
Support vector machines (SVM) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM...
Robust EEG Channel Selection Across Subjects for Brain Computer Interfaces (2005)
Schröder, M., Lal, T.N., Hinterberger, T., Bogdan, M., Hill, J., Birbaumer, N., ...
Most EEG-based Brain Computer Interface (BCI) paradigms come along with specific electrode positions, e.g.~for a visual based BCI electrode positions close to the primary visual cortex are used. For...
Jung, T., Herrera, L., Schölkopf, B., Cabestany, J., Prieto, A., Sandoval, F., ...
In this paper we report the results obtained using a kernel-based approach to predict the temporal development of four response signals in the process control of a glass melting tank with 16 input...
Iterative Kernel Principal Component Analysis for Image Modeling (2005)
Kim, K.I., Franz, M., Schölkopf, B.
In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form...
Building Sparse Large Margin Classifiers (2005)
Wu, M., Schölkopf, B., BakIr, G., Raedt, L. De, Wrobel, S.
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The added constraint...
Implicit Surface Modelling as an Eigenvalue Problem (2005)
Walder, C., Chapelle, O., Schölkopf, B., Raedt, L. De, Wrobel, S.
We discuss the problem of fitting an implicit shape model to a set of points sampled from a co-dimension one manifold of arbitrary topology. The method solves a non-convex optimisation problem in the...
Regularization on Discrete Spaces (2005)
We consider the classification problem on a finite set of objects. Some of them are labeled, and the task is to predict the labels of the remaining unlabeled ones. Such an estimation problem is...
An Auditory Paradigm for Brain--Computer Interfaces (2005)
Hill, N.J., Lal, T.N., Bierig, K., Birbaumer, N., Schölkopf, B., Saul, L.K., ...
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm...
Kernel Methods for Implicit Surface Modeling (2005)
Schölkopf, B., Giesen, J., Spalinger, S., Saul, L.K., Weiss, Y., Bottou, L., ...
We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space...
Kernel Constrained Covariance for Dependence Measurement (2005)
Gretton, A., Smola, A.J., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., ...
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables....
Kernel Methods for Measuring Independence (2005)
Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the...
A tutorial on v-support vector machines (2005)
Chen, P., Lin, C., Schölkopf, B.
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM,...
A Brain Computer Interface with Online Feedback based on Magnetoencephalography (2005)
Lal, T.N., Schröder, M., Hill, J., Preissl, H., Hinterberger, T., Mellinger, J., ...
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a...
RASE: Recognition of alternatively spliced exons in C.elegans (2005)
Rätsch, G., Sonnenburg, S., Schölkopf, B.
Motivation: Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human...
G. Rätsch, S. Sonnenburg, B. Schölkopf
Vol. 21 Suppl. 1 2005, pages i369–i377 doi:10.1093/bioinformatics/bti1053
Large scale genomic sequence svm classifiers (2005)
S. Sonnenburg, G. Rätsch, B. Schölkopf
Abstract. In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve sufficiently high...
RASE: recognition of alternatively spliced exons in C.elegans (2005)
Rätsch, G., Sonnenburg, S., Schölkopf, B.
Motivation: Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human...
Sample based generalisation bounds (2004)
Williamson, R.C., Shawe-Taylor, J., Schölkopf, B., Smola, A.
Sample based generalisation bounds (2004)
Williamson, R.C., Shawe-Taylor, J., Schölkopf, B., Smola, A.
Learning with Local and Global Consistency (2004)
Zhou,D., Bousquet,O., Lal,T.N., Weston,J., Schölkopf,B.
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on Data Manifolds (2004)
Zhou,D., Weston,J., Gretton,A., Bousquet,O., Schölkopf,B.
The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Learning from Labeled and Unlabeled Data Using Random Walks (2004)
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels...
A Regularization Framework for Learning from Graph Data (2004)
The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is...
Kernel Hebbian Algorithm for single-frame super-resolution (2004)
Kim,K.I., Franz,M.O., Schölkopf,B.
This paper presents a method for single-frame image super-resolution using an unsupervised learning technique. The required prior knowledge about the high-resolution images is obtained from Kernel...
Efficient Approximations for Support Vector Machines in Object Detection (2004)
Kienzle,W., Bakir,G.H., Franz,M.O., Schölkopf,B.
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is...
A kernel view of the dimensionality reduction of manifolds (2004)
Ham,J., Lee,D.D., Mika,S., Schölkopf,B.
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local...
Semi-supervised kernel regression using whitened function classes (2004)
Franz,M.O., Kwon,Y., Rasmussen,C.E., Schölkopf,B.
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique...
Implicit estimation of Wiener series (2004)
The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation...
Prediction on Spike Data Using Kernel Algorithms (2004)
Eichhorn,J., Tolias,A.S., Zien,A., Kuss,M., Rasmussen,C.E., Weston,J., ...
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a...
Protein Functional Class Prediction with a Combined Graph (2004)
Shin,H., Tsuda,K., Schölkopf,B.
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as...
Learning to Find Pre-Images (2004)
Bakir,G.H., Weston,J., Schölkopf,B.
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Multivariate Regression via Stiefel Manifold Constraints (2004)
Bakir,G.H., Gretton,A., Franz,M.O., Schölkopf,B.
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered...
A Compression Approach to Support Vector Model Selection (2004)
Von Luxburg,U., Bousquet,O., Schölkopf,B.
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization...
Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm (2004)
Hill,N.J., Lal,T.N., Schröder,M., Hinterberger,T., Birbaumer,N., Schölkopf,B.
Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface (2004)
Hill,N.J., Lal,T.N., Bierig,K., Birbaumer,N., Schölkopf,B.
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm...
Support Vector Channel Selection in BCI (2004)
Lal,T.N., Schröder,M., Hinterberger,T., Weston,J., Bogdan,M., Birbaumer,N., ...
Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying...
Learning with Local and Global Consistency (2004)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B., Thrun, S., ...
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on Data Manifolds (2004)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B., Thrun, S., ...
The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Learning from Labeled and Unlabeled Data Using Random Walks (2004)
Zhou, D., Schölkopf, B., Rasmussen, C.E., Bülthoff, H.H., Giese, M.A., Schölkopf, B.
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels...
A Regularization Framework for Learning from Graph Data (2004)
The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is...
Semi-Supervised Protein Classification using Cluster Kernels (2004)
Weston, J., Leslie, C., Zhou, D., Elisseeff, A., Noble, W.S., Thrun, S., ...
A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art...
Warped Gaussian Processes (2004)
Snelson, E., Rasmussen, C.E., Ghahramani, Z., Thrun, S., Saul, L., Schölkopf, B.
We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning...
A Primer on Kernel Methods (2004)
Vert, J.P., Tsuda, K., Schölkopf, B., Schölkopf, B., Tsuda, K., Vert, J.P.
Kernel Hebbian Algorithm for single-frame super-resolution (2004)
Kim, K.I., Franz, M.O., Schölkopf, B., Leonardis, A., Bischof, H.
This paper presents a method for single-frame image super-resolution using an unsupervised learning technique. The required prior knowledge about the high-resolution images is obtained from Kernel...
Efficient Approximations for Support Vector Machines in Object Detection (2004)
Kienzle, W., Bakir, G.H., Franz, M.O., Schölkopf, B., Rasmussen, C. E., Bülthoff, H. H., ...
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is...
Hilbertian Metrics on Probability Measures and their Application in SVM's (2004)
Hein, H., Lal, T.N., Bousquet, O., Rasmussen, C. E., Bülthoff, H. H., Giese, M., ...
The goal of this article is to investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great...
A kernel view of the dimensionality reduction of manifolds (2004)
Ham, J., Lee, D.D., Mika, S., Schölkopf, B., Greiner, R., Schuurmans, D.
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local...
Insights from Machine Learning Applied to Human Visual Classification (2004)
Graf, A.B.A., Wichmann, F.A., Thrun, S., Saul, L., Schölkopf, B.
Semi-supervised kernel regression using whitened function classes (2004)
Franz, M.O., Kwon, Y., Rasmussen, C.E., Schölkopf, B., Rasmussen, C. E., Bülthoff, H. H., ...
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique...
Implicit estimation of Wiener series (2004)
Franz, M.O., Schölkopf, B., Barros, A., Principe, J., Larsen, J., Adali, T., ...
The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation...
Prediction on Spike Data Using Kernel Algorithms (2004)
Eichhorn, J., Tolias, A.S., Zien, A., Kuss, M., Rasmussen, C.E., Weston, J., ...
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a...
Protein Functional Class Prediction with a Combined Graph (2004)
Shin, H., Tsuda, K., Schölkopf, B.
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as...
EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods (2004)
Schröder, M., Lal, T.N., Bogdan, M., Schölkopf, B., Bülthoff, H.H., Mallot, H.A., ...
Gaussian Processes in Reinforcement Learning (2004)
Rasmussen, C.E., Kuss, M., Thrun, S., Saul, L. K., Schölkopf, B.
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP model allows evaluation...
Measure Based Regularization (2004)
Bousquet, O., Chapelle, O., Hein, M., Thrun, S., Saul, L., Schölkopf, B.
We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into...
Learning to Find Pre-Images (2004)
Bakir, G.H., Weston, J., Schölkopf, B., Thrun, S., Saul, L., Schölkopf, B.
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of...
Multivariate Regression via Stiefel Manifold Constraints (2004)
Bakir, G.H., Gretton, A., Franz, M.O., Schölkopf, B., Rasmussen, C. E., Bülthoff, H. H., ...
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered...
A Compression Approach to Support Vector Model Selection (2004)
Von Luxburg, U., Bousquet, O., Schölkopf, B.
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization...
Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm (2004)
Hill, N.J., Lal, T.N., Schröder, M., Hinterberger, T., Birbaumer, N., Schölkopf, B., ...
Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface (2004)
Hill, N.J., Lal, T.N., Bierig, K., Birbaumer, N., Schölkopf, B.
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm...
Support Vector Channel Selection in BCI (2004)
Lal, T.N., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., ...
Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying...
Sample based generalisation bounds (2004)
Williamson, R.C., Shawe-Taylor, J., Schölkopf, B., Smola, A.
Support Vector Channel Selection in BCI (2004)
Lal, T. M., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., ...
Feature selection and transduction for prediction of molecular bioactivity for drug design (2003)
Weston,J., Pérez-Cruz,F., Bousquet,O., Chapelle,O., Elisseeff,A., Schölkopf,B.
Use of the Zero-Norm with Linear Models and Kernel Methods (2003)
Weston,J., Elisseeff,A., Schölkopf,B., Tipping,M.
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature...
Mika,S., Rätsch,G., Weston,J., Schölkopf,B., Smola,A.J.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh...
Distance-based classification with Lipschitz functions (2003)
Von Luxburg, U., Bousquet, O., Schölkopf, B., Warmuth, M.K.
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a...
Extension of the nu-SVM range for classification (2003)
Perez-Cruz, F., Weston, J., Herrmann, D.J.L., Schölkopf, B., Suykens, J., Horvath, G., ...
Feature selection and transduction for prediction of molecular bioactivity for drug design (2003)
Weston, J., Pérez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.
Dealing with large Diagonals in Kernel Matrices (2003)
Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.S.
Use of the Zero-Norm with Linear Models and Kernel Methods (2003)
Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.
We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature...
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A.J.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh...
Mika, S., Ratsch, G., Weston, J., Schölkopf, B., Smola, A.
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh...
Extraction of Features Using M-Band Wavelet Packet Frame and Their . . . (2003)
M. Acharyya, B. Schölkopf, K. Sung, F. Girosi, P. Niyogi, ...
In this paper, we propose a scheme for segmentation of multitexture images. The methodology involves extraction of texture features using an overcomplete wavelet decomposition scheme called discrete...
A Kernel Approach for Learning from almost Orthogonal Patterns (2002)
Schölkopf, B., Weston, J., Eskin, E., Leslie, C., Noble, W.S., Elomaa, T., ...
Constructing boosting algorithms from SVMs: An application to one-class classification (2002)
Ratsch, G., Mika, S., Schölkopf, B.
We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation...
Estimating the Support of a High-Dimensional Distribution (2001)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point...
Estimating the Support of a High-Dimensional Distribution (2001)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point...
Estimating the Support of a High-Dimensional Distribution (2001)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a “simple” subset S of input space such that the probability that a test point...
SVM and boosting. One class (2000)
Rätsch, G., Schölkopf, B., Mika, S.
We show via an equivalence of mathematical programs that a Support Vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation...
An improved training algorithm for kernel fisher discriminants (2000)
Mika, S., Smola, A., Schölkopf, B.
We present a fast training algorithm for the kernel Fisher discriminant classifier. It uses a greedy approximation technique and has an empirical scaling behavior which improves upon the state of the...
Time series prediction using SV regression and neural networks (2000)
Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.
Invariant feature extraction and classification in kernel spaces (2000)
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A.J.
Engineering support vector machine kernels that recognize translation initiation sites (2000)
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T.
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
SVM and boosting. One class (2000)
Rätsch, G., Schölkopf, B., Mika, S.
We show via an equivalence of mathematical programs that a Support Vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation...
Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites (2000)
A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
Using Support Vector Machines for Time Series Prediction (2000)
A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, V. Vapnik
This paper is an extended version of [12]. Generic author design sample pages 2000/07/31 03:05
Engineering support vector machine kernels that recognize translation initiation sites (2000)
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T.
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called...
Using support vector machines for time series prediction (1999)
Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.
Generalization Bounds via the Eigenvalues of the Gram Matrix (1999)
Schölkopf, B., Shawe-Taylor, J., Smola, A.J., Williamson, R.
Classification on proximity data with LP-machines (1999)
Graepel, T., Herbrich, R., Schölkopf, B., Smola, A.J., Bartlett, P.L., ...
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A.J., ...
Kernel PCA and de-noising in feature spaces (1999)
Mika, S., Schölkopf, B., Smola, A.J., Scholz, M., Rätsch, G.
Input space vs. feature space in kernel-based methods (1999)
Schölkopf, B., Mika, S., Burges, C.J., Knirsch, P., Rätsch, G., ...
Classifying LEP Data with Support Vector Algorithms (1999)
P. Vannerem, B. Schölkopf, A. Smola, S. Söldner-Rembold
We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data...
Support Vector Machine - Reference Manual (1998)
C. Saunders, M. O. Stitson, J. Weston, Royal Holloway, L. Bottou, B. Schölkopf, ...
this document will describe these programs. To find out more about SVMs, see the bibliography. We will not describe how SVMs work here.
Asymptotically Optimal Choice of epsilon-Loss for Support Vector Machines (1998)
A. J. Smola, N. Murata, B. Schölkopf
Under the assumption of asymptotically unbiased estimators we show that there exists a nontrivial choice of the insensitivity parameter in Vapnik's "--insensitive loss function which scales...
Support Vector Regression with Automatic Accuracy Control (1998)
B. Schölkopf, P. Bartlett, A. Smola, R. Williamson
A new algorithm for Support Vector regression is proposed. For a priori chosen , it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction of the data points...
We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above...