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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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 inExpand
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A tutorial on support vector regression
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 SVExpand
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Estimating the Support of a High-Dimensional Distribution
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 drawnExpand
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Learning with Local and Global Consistency
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervisedExpand
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A Kernel Two-Sample Test
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. Our test statistic isExpand
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A Kernel Method for the Two-Sample-Problem
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into aExpand
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A gene expression map of Arabidopsis thaliana development
Regulatory regions of plant genes tend to be more compact than those of animal genes, but the complement of transcription factors encoded in plant genomes is as large or larger than that found inExpand
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Kernel Principal Component Analysis
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 inExpand
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New Support Vector Algorithms
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 beExpand
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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 normExpand
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