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A tutorial on ν ‐support vector machines
Author(s) -
Chen PaiHsuen,
Lin ChihJen,
Schölkopf Bernhard
Publication year - 2005
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.537
Subject(s) - support vector machine , statistical learning theory , computer science , kernel (algebra) , artificial intelligence , statistical learning , feature (linguistics) , kernel method , feature vector , machine learning , theoretical computer science , mathematics , discrete mathematics , philosophy , linguistics
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so‐called ν ‐SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.