JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Polychotomous Machines;
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Polychotomous Machines;
Koo, Ja-Yong; Park, Heon Jin; Choi, Daewoo;
  PDF(new window)
 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;
 Language
English
 Cited by
 References
1.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2002.

2.
The Elements of Statistical Learning, 2001.

3.
Learning Kernel Classifiers: Theroy and Algorithms, 2001.

4.
Technical Report 1014. Department of Statistics, 1999.

5.
Advances in Large Margin Classifiers, 1999.

6.
Linear Statistical Inference and Its Applications(2nd edn.), 1973.

7.
Advances in Neural Information Processing Systems, 1999. vol.11.

8.
Advances in Neural Information Processing Systems, 2001. vol.14.

9.
Statistical Learning Theory, 1998.