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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 by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
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Learning with kernels
TLDR
This book is intended to be a guide to the art of self-consistency and should not be used as a substitute for a comprehensive guide to self-confidence.
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A tutorial on support vector regression
TLDR
This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
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A Kernel Two-Sample Test
TLDR
This work proposes a framework for analyzing and comparing distributions, which is used to construct statistical tests to determine if two samples are drawn from different distributions, and presents two distribution free tests based on large deviation bounds for the maximum mean discrepancy (MMD).
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Estimating the Support of a High-Dimensional Distribution
TLDR
The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data by carrying out sequential optimization over pairs of input patterns and providing a theoretical analysis of the statistical performance of the algorithm.
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Hierarchical Attention Networks for Document Classification
TLDR
Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin.
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Support Vector Regression Machines
TLDR
This work compares support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space and expects that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.
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A Kernel Method for the Two-Sample-Problem
TLDR
This work proposes two statistical tests to determine if two samples are from different distributions, and applies this approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where the test performs strongly.
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Kernel Principal Component Analysis
TLDR
A new method for performing a nonlinear form of Principal Component Analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
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Measuring Statistical Dependence with Hilbert-Schmidt Norms
We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm…
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