Kernel Methods

Xinhua Zhang

doi:10.1007/978-0-387-30164-8_430

Kernel Methods

Xinhua Zhang

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary › peer-review

Abstract

Kernel methods refer to a class of techniques that employ positive definite kernels. At an algorithmic level, its basic idea is quite intuitive: implicitly map objects to high-dimensional feature spaces, and then directly specify the inner product there.

Original language	English
Title of host publication	Encyclopedia of Machine Learning
Editors	Claude Sammut & Geoffrey I.Webb
Place of Publication	New York
Publisher	Springer
Pages	5pp
Volume	6
ISBN (Print)	9780387307688
DOIs	https://doi.org/10.1007/978-0-387-30164-8_430
Publication status	Published - 2010

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10.1007/978-0-387-30164-8_430

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title = "Kernel Methods",

abstract = "Kernel methods refer to a class of techniques that employ positive definite kernels. At an algorithmic level, its basic idea is quite intuitive: implicitly map objects to high-dimensional feature spaces, and then directly specify the inner product there.",

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language = "English",

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DO - 10.1007/978-0-387-30164-8_430

M3 - Entry for encyclopedia/dictionary

SN - 9780387307688

VL - 6

SP - 5pp

BT - Encyclopedia of Machine Learning

A2 - null, Claude Sammut & Geoffrey I.Webb

PB - Springer

CY - New York

ER -

Kernel Methods

Abstract

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