[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2003.10.1.225

Polychotomous Machines;

Koo, Ja-Yong (Department of Statistics, Inha University)
Park, Heon Jin (Department of Statistics, Inha University)
Choi, Daewoo (Department of Statistics, Hankuk University of Foreign Studies)

Publication Information

Communications for Statistical Applications and Methods / v.10, no.1, 2003 , pp. 225-232 More about this Journal

Abstract

The support vector machine (SVM) is becoming increasingly popular in classification. The import vector machine (IVM) has been introduced for its advantages over SMV. This paper tries to improve the IVM. The proposed method, which is referred to as the polychotomous machine (PM), uses the Newton-Raphson method to find estimates of coefficients, and the Rao and Wald tests, respectively, for addition and deletion of import points. Because the PM basically follows the same addition step and adopts the deletion step, it uses, typically, less import vectors than the IVM without loosing accuracy. Simulated and real data sets are used to illustrate the performance of the proposed method.

Keywords

classification; import vector; maximum likelihood; Newton-Raphson; reproducing kernel; stepwise algorithm;

Citations & Related Records

Reference

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