Mercer’s Theorem, Feature Maps, and Smoothing

We study Mercer’s theorem and feature maps for several positive definite kernels that are widely used in practice. The smoothing properties of these kernels will also be explored.

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Bibliographic Details
Published inLearning Theory pp. 154 - 168
Main Authors Minh, Ha Quang, Niyogi, Partha, Yao, Yuan
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Gaussian Kernel
Orthonormal Basis
Polynomial Kernel
Reproduce Kernel Hilbert Space
Spherical Harmonic
Smoothing
Artificial intelligence
Positive definite matrix
Online AccessGet full text
ISBN3540352945
9783540352945
ISSN0302-9743
1611-3349
DOI10.1007/11776420_14

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Summary:We study Mercer’s theorem and feature maps for several positive definite kernels that are widely used in practice. The smoothing properties of these kernels will also be explored.
ISBN:3540352945
9783540352945
ISSN:0302-9743
1611-3349
DOI:10.1007/11776420_14