Adaptive support vector machine for time-varying data streams using martingale

Shen Shyang Ho, Harry Wechsler

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

A martingale framework is proposed to enable support vector machine (SVM) to adapt to time-varying data streams. The adaptive SVM is a one-pass incremental algorithm that (i) does not require a sliding window on the data stream, (ii) does not require monitoring the performance of the classifier as data points are streaming, and (iii) works well for high dimensional, multi-class data streams. Our experiments show that the novel adaptive SVM is effective at handling time-varying data streams simulated using both a synthetic dataset and a multiclass real dataset.

Original languageEnglish (US)
Pages (from-to)1606-1607
Number of pages2
JournalIJCAI International Joint Conference on Artificial Intelligence
Publication statusPublished - Dec 1 2005
Externally publishedYes
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: Jul 30 2005Aug 5 2005

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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