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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
TLDR
A new method for performing a nonlinear form of principal component analysis in high-dimensional feature spaces, related to input space by some nonlinear map. Expand
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Learning with kernels
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) withoutExpand
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A tutorial on support vector regression
TLDR
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Expand
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Estimating the Support of a High-Dimensional Distribution
TLDR
We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. Expand
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A Kernel Two-Sample Test
TLDR
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Expand
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Hierarchical Attention Networks for Document Classification
We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure ofExpand
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A Kernel Method for the Two-Sample-Problem
TLDR
We propose two statistical tests to determine if two samples are from different distributions. Expand
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Support Vector Regression Machines
TLDR
A new regression technique based on Vapnik's concept of support vectors is introduced. Expand
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Advances in kernel methods: support vector learning
Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vectorExpand
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Kernel Principal Component Analysis
TLDR
A new method for performing a nonlinear form of Principal Component Analysis is proposed. Expand
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