NDLI: Large Margin Classifier Based on Hyperdisks

Content Provider	IEEE Xplore Digital Library
Author	Cevikalp, H.
Copyright Year	2011
Abstract	This paper introduces a binary large margin classifier that approximates each class with an hyper disk constructed from its training samples. For any pair of classes approximated with hyper disks, there is a corresponding linear separating hyper plane that maximizes the margin between them, and this can be found by solving a convex program that finds the closest pair of points on the hyper disks. More precisely, the best separating hyper plane is chosen to be the one that is orthogonal to the line segment connecting the closest points on the hyper disks and at the same time bisects the line. The method is extended to the nonlinear case by using the kernel trick, and the multi-class classification problems are dealt with constructing and combining several binary classifiers as in Support Vector Machine (SVM) classifier. The experiments on several databases show that the proposed method compares favorably to other popular large margin classifiers.
Starting Page	370
Ending Page	375
File Size	1369169
Page Count	6
File Format	PDF
ISBN	9781457721342
DOI	10.1109/ICMLA.2011.86
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	support vector machines Approximation methods classification convex hull hyperdisk quadratic programming Optimization Support vector machines Training Accuracy Databases kernel methods Kernel large margin classifier
Content Type	Text
Resource Type	Article

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