What is a support vector machine?

被引：2682

作者：

Noble, William S.

机构：

[1] Univ Washington, Dept Genome Sci, Seattle, WA 98195 USA

[2] Univ Washington, Dept Comp Sci, Seattle, WA 98195 USA

[3] Univ Washington, Dept Engn, Seattle, WA 98195 USA

来源：

NATURE BIOTECHNOLOGY | 2006年 / 24卷 / 12期

关键词：

D O I：

10.1038/nbt1206-1565

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?

引用

页码：1565 / 1567

页数：3

共 8 条

[1]

[Anonymous], 1992, TRAINING ALGORITHM O

[2]

Bordes A, 2005, J MACH LEARN RES, V6, P1579

[3] Support vector machine classification and validation of cancer tissue samples using microarray expression data [J].

Furey, TS ;

Cristianini, N ;

Duffy, N ;

Bednarski, DW ;

Schummer, M ;

Haussler, D .

BIOINFORMATICS, 2000, 16 (10) :906-914

[4] Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring [J].

Golub, TR ;

Slonim, DK ;

Tamayo, P ;

Huard, C ;

Gaasenbeek, M ;

Mesirov, JP ;

Coller, H ;

Loh, ML ;

Downing, JR ;

Caligiuri, MA ;

Bloomfield, CD ;

Lander, ES .

SCIENCE, 1999, 286 (5439) :531-537

[5] Gene selection for cancer classification using support vector machines [J].

Guyon, I ;

Weston, J ;

Barnhill, S ;

Vapnik, V .

MACHINE LEARNING, 2002, 46 (1-3) :389-422

[6]

Noble WS., 2004, Kernel methods in computational biology, V14, P71, DOI DOI 10.1049/EL:19981092

[7] Multiclass cancer diagnosis using tumor gene expression signatures [J].

Ramaswamy, S ;

Tamayo, P ;

Rifkin, R ;

Mukherjee, S ;

Yeang, CH ;

Angelo, M ;

Ladd, C ;

Reich, M ;

Latulippe, E ;

Mesirov, JP ;

Poggio, T ;

Gerald, W ;

Loda, M ;

Lander, ES ;

Golub, TR .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2001, 98 (26) :15149-15154

[8]

Vapnik V., 1963, AUTOMAT REM CONTR, V24, P774, DOI [DOI 10.12691/JGG-2-3-9, DOI 10.1162/NECO.1992.4.6.888]

← 1 →