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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Incorporating a Priori Knowledge from Detractor Points into Support Vector Classification
verfasst von : Marcin Orchel
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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In this article, we extend the idea of a priori knowledge in the form of detractor points presented recently for Support Vector Classification. We show that detractor points can belong to the new type of support vectors – training samples which lie outside a margin bounded region. We present the new application for a priori knowledge from detractor points – improving generalization performance of Support Vector Classification while reducing a complexity of a model by removing a bunch of support vectors. The experiments show that indeed the new type of a priori knowledge improves generalization performance of reduced models. The tests were performed on selected classification data sets, and on stock price data from public domain repositories.