Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 95, Issue 2, Pages 188-198Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2008.10.007
Keywords
Support vector machines; Pattern recognition; Regression; Nonlinearity
Categories
Funding
- National Nature Foundation Committee of P.R. China [20475066, 10771217]
- ministry of science and technology of China [2006DFA41090, 2007DFA40680]
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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.
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