Incremental support vector learning: Analysis, implementation and applications

Pavel Laskov, Christian Gehl, Stefan Krüger, Klaus Muller

Research output: Contribution to journalArticle

254 Citations (Scopus)

Abstract

Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.

Original languageEnglish
Pages (from-to)1909-1936
Number of pages28
JournalJournal of Machine Learning Research
Volume7
Publication statusPublished - 2006 Sep 1
Externally publishedYes

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Keywords

  • Drug discovery
  • Incremental SVM
  • Intrusion detection
  • Online learning

ASJC Scopus subject areas

  • Artificial Intelligence

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