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...
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...
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...
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...
Gene selection via the BAHSIC family of algorithms (2007)
Song, L., Bedo, J., Borgwardt, K.M., Gretton, A., Smola, A.
Motivation: Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are...
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 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...
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...
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...
Learning High-Order MRF Priors of Color Images (2006)
McAuley, J., Caetano, T., Smola, A., Franz, M.O.
In this paper, we use large neighborhood Markov random fields to learn rich prior models of color images. Our approach extends the monochromatic Fields of Experts model (Roth and Blackwell, 2005} to...
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...
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...
Measuring Statistical Dependence with Hilbert-Schmidt Norms (2005)
Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B.
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm...
Learning the Kernel with Hyperkernels (2005)
Ong, C.S., Smola, A., Williamson, R.
This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing...
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...
Learning with Non-Positive Kernels (2004)
Ong, C.S., Mary, X., Canu, S., Smola, A.
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that is, kernels which are not positive semidefinite. They do not satisfy Mercer’s condition and they...
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.
Sample based generalisation bounds (2004)
Williamson, R.C., Shawe-Taylor, J., Schölkopf, B., Smola, A.
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...
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...
arc ensemble learning in the presence of outliers (2000)
G. Ratsch, B. Scholkopf, A. Smola, T. Onoda, S. Mika
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Classifying LEP Data with Support Vector Algorithms (1999)
Vannerem, P., Schoelkopf, B., Smola, A., Soldner-Rembold, S.
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...
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)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf., B., Smola, A.
Support Vector Machine - Reference Manual (1998)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf., B., Smola, A.
Support Vector Machine - Reference Manual (1998)
Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf., B., Smola, A.
Support vector machine reference manual (1998)
C. Saunders, M. O. Stitson, J. Weston, Royal Holloway, L. Bottou, B. Scholkopf, ...
The Support Vector Machine (SVM) is a new type of learning machine. The SVM is a general architecture that can be applied to pattern recognition, regression estimation and other problems. The...
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.
Support Vector Machine - Reference Manual (1998)
C. Saunders, M. O. Stitson, J. Weston, Royal Holloway, L. Bottou, B. Scholkopf, ...
this document will describe these programs. To find out more about SVMs, see the bibliography. We will not describe how SVMs work here.
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...