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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. Expand
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. Expand
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. Expand
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). Expand
Learning with Local and Global Consistency
TLDR
A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Expand
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. Expand
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
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. Expand
A gene expression map of Arabidopsis thaliana development
TLDR
Examining the expression patterns of large gene families, it is found that they are often more similar than would be expected by chance, indicating that many gene families have been co-opted for specific developmental processes. Expand
Support Vector Method for Novelty Detection
TLDR
The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data and is regularized by controlling the length of the weight vector in an associated feature space. Expand
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