#### Filter Results:

- Full text PDF available (172)

#### Publication Year

1996

2018

- This year (6)
- Last 5 years (74)
- Last 10 years (140)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

- Bernhard SchÃ¶lkopf, Alexander J. Smola, Klaus-Robert MÃ¼ller
- Neural Computation
- 1998

A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components inâ€¦ (More)

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) withoutâ€¦ (More)

- Bernhard SchÃ¶lkopf, Alexander J. Smola
- Adaptive computation and machine learning series
- 2002

- Bernhard SchÃ¶lkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson
- Neural Computation
- 2001

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 drawnâ€¦ (More)

- Alexander J. Smola, Bernhard SchÃ¶lkopf
- Statistics and Computing
- 2004

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SVâ€¦ (More)

A new regression technique based on Vapnikâ€™s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regressionâ€¦ (More)

A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal components in high{â€¦ (More)

- Bernhard SchÃ¶lkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett
- Neural Computation
- 2000

We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter lets one effectively control the number of support vectors. While this can beâ€¦ (More)

- Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard SchÃ¶lkopf, Alexander J. Smola
- Journal of Machine Learning Research
- 2012

We propose a framework for analyzing and comparing distribu tions, which we use to construct statistical tests to determine if two samples are drawn from dif ferent distributions. Our test statisticâ€¦ (More)

We propose a framework for analyzing and comparing distributions, allowing us to design statistical tests to determine if two samples are drawn from different distributions. Our test statistic is theâ€¦ (More)