Support vector machines and its applications in chemistry

被引：260

作者：

Li, Hongdong ^{[1
]}

Liang, Yizeng ^{[1
]}

Xu, Qingsong ^{[2
]}

机构：

[1] Cent S Univ, Coll Chem & Chem Engn, Res Ctr Modernizat Tradit Chinese Med, Changsha 410083, Peoples R China

[2] Cent S Univ, Sch Math Sci, Changsha 410083, Peoples R China

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2009年 / 95卷 / 02期

关键词：

Support vector machines; Pattern recognition; Regression; Nonlinearity; PARTIAL LEAST-SQUARES; NEAR-INFRARED SPECTROSCOPY; RADIAL BASIS FUNCTIONS; DISCRIMINANT-ANALYSIS; PATTERN-RECOGNITION; VARIABLE-SELECTION; CROSS-VALIDATION; HEURISTIC METHOD; NEURAL-NETWORKS; DRUG DESIGN;

D O I：

10.1016/j.chemolab.2008.10.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Support vector machines (SVMs) are a promising machine learning method originally developed for pattern recognition problem based on structural risk minimization. Functionally, SVMs can be divided into two categories: support vector classification (SVC) machines and support vector regression (SVR) machines. According to this classification, their basic elements and algorithms are discussed in some detail and selected applications on two real world datasets and two simulated datasets are conducted to elucidate the good generalization performance of SVMs, specially good for treating the data of some nonlineartiy. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：188 / 198

页数：11

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