Automatically assigned DDC number: 00631

Manually assigned DDC number: 006312

Number of references: 16

Title: Minimal Kernel Classifiers

Author:

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Subject: Glenn Fung,Olvi L. Mangasarian,Alexander J. Smola Minimal Kernel Classifiers

Description: A finite concave minimization algorithm is proposed for constructing kernel classifiers that use a minimal number of data points both in generating and characterizing a classifier. The algorithm is theoretically justified on the basis of linear programming perturbation theory and a leave-one-out error bound as well as e#ective computational results on seven real world datasets. A nonlinear rectangular kernel is generated by systematically utilizing as few of the data as possible both in training and in characterizing a nonlinear separating surface. This can result in substantial kernel size reduction (over 87% in six of the seven public datasets tested on) with test set correctness equal to that obtained by using a full kernel. To eliminate data points, the proposed approach makes use of a novel loss function, the "pound" function () # , which is a linear combination of the 1-norm and the step function that measures both the magnitude and the presence of any error. The e#ectiveness of ...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2000-11-28

Format: pdf

Identifier: http://citeseer.ist.psu.edu/387182.html

Source: http://www.cs.wisc.edu/~gfung/00-08.pdf

Language: en

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