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...
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...
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...
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...
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...
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...
Colored Maximum Variance Unfolding (2008)
Song, L., Smola, A.J., Borgwardt, K., Gretton, A., Platt, J. C., Koller, D., ...
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while...
Robust Ensemble Learning for Data Mining G. Ratsch 1, B. Scholkopf 2 (2007)
A. J. Smola, S. Mika, T. Onoda
1
A. J. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, V. Vapnik
Abstract. Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with...
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...
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...
Supervised Feature Selection via Dependence Estimation (2007)
Song, L., Smola, A.J., Gretton, A., Borgwardt, K.M., Bedo, J.
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that...
A Dependence Maximization View of Clustering (2007)
Song, L., Smola, A.J., Gretton, A., Borgwardt, K.M.
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion...
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...
Large-scale multiclass transduction (2006)
Gärtner, T., Le, Q.V., Burton, S., Smola, A.J., Vishwanathan, V.
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....
Protein Function prediction via Graph Kernels. (2005)
Borgwardt, K. M., Ong, Cheng Soon, Schönauer, S., Vishwanathan, S., Smola, A. J., Kriegel, H.
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., 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...
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...
Estimating the Support of a High-Dimensional Distribution (2001)
Sch"olkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Estimating the Support of a High-Dimensional Distribution (2001)
Sch"olkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
Regularized Principal Manifolds (2001)
Smola, A.J., Mika, S., Schoelkopf, B., Williamson, R.C.
Many settings of unsupervised learning can be viewed as quantization problems - the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as...
Estimating the Support of a High-Dimensional Distribution (2001)
Sch"olkopf, B., Platt, J.C., Shawe-Taylor, J.S., Smola, A.J., Williamson, R.C.
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.
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
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., ...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural networks. One of the most important ingredients are kernels, i.e. the concept of transforming...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural networks. One of the most important ingredients are kernels, i.e. the concept of transforming...
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...
We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above...
Predicting Time Series with Support Vector Machines (1997)
Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.
Using Support Vector Machines for Time Series Prediction (1997)
Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.
Predicting Time Series with Support Vector Machines (1997)
A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, V. Vapnik
. Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two dierent cost functions for Support Vectors: training with (i) an...
Predicting Time Series with Support Vector Machines (1997)
A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, V. Vapnik
. Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an ffl...
Predicting Time Series with Support Vector Machines (1997)
Muller Smola, A. J. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, ...
. Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an ffl...
Predicting time series with support vector machines (1997)
A. J. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, V. Vapnik
Abstract. Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two di erent cost functions for Support Vectors: training with (i)...