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Using domain-specific knowledge in generalization error bounds for support vector machine learning

Eryarsoy, Enes and Koehler, Gary J. and Aytuğ, Haldun (2008) Using domain-specific knowledge in generalization error bounds for support vector machine learning. (Accepted/In Press)

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Official URL: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8S-4TDVM77-1&_user=691258&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_version=1&_urlVersion=0&_userid=691258&md5=fde39406fa66b0225705fe2f1144e4b7

Abstract

In this study we describe a methodology to exploit a specific type of domain knowledge in order to find tighter error bounds on the performance of classification via Support Vector Machines. The domain knowledge we consider is that the input space lies inside of a specified convex polytope. First, we consider prior knowledge about the domain by incorporating upper and lower bounds of attributes.We then consider a more general framework that allows us to encode prior knowledge in the form of linear constraints formed by attributes. By using the ellipsoid method from optimization literature, we show that, this can be exploited to upper bound the radius of the hyper-sphere that contains the input space, and enables us to tighten generalization error bounds. We provide a comparative numerical analysis and show the effectiveness of our approach.

Item Type:Article
Uncontrolled Keywords:Prior knowledge; Support vector machines; Ellipsoid method; Error bounds; Fat-shattering dimension
Subjects:UNSPECIFIED
ID Code:10115
Deposited By:Enes Eryarsoy
Deposited On:07 Nov 2008 09:30
Last Modified:26 Mar 2009 15:40

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