Abstract
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.
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Orchel, M. (2011). Incorporating a Priori Knowledge from Detractor Points into Support Vector Classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_35
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DOI: https://doi.org/10.1007/978-3-642-20267-4_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20266-7
Online ISBN: 978-3-642-20267-4
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