The p-Center machine

Author

Brückner, Michael

Author_Institution

Dept. of Comput. Sci., Chemnitz Univ. of Technol., Germany

Volume

2

fYear

2005

fDate

31 July-4 Aug. 2005

Firstpage

1000

Abstract

We present a new approach to find an optimal large margin classifier based on the p-center which was proposed by Moretti in 2003. Starting with the p-Center of a general polytope, we extend this definition to a polyhedral cone, and introduce an algorithm approximating the p-Center of the version space, which we call p-Center machine (PCM). In addition, we present a large-scale and a soft boundary version of the PCM, and compare their performance to the support vector machine and the Bayes point machine. It turns out that the p-Center is close to the Bayes point and is similar in performance to the support vector machine as well as the Bayes point machine. Additionally, the proposed algorithm is highly parallelizable and thus very efficient in terms of computational effort.

Keywords

Bayes methods; pattern classification; support vector machines; Bayes point machine; optimal large margin classifier; p-Center machine; polyhedral cone; support vector machine; Chemical technology; Computer science; Concurrent computing; Electronic mail; Extraterrestrial measurements; Kernel; Large-scale systems; Phase change materials; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on

Print_ISBN

0-7803-9048-2

Type

conf

DOI

10.1109/IJCNN.2005.1555989

Filename

1555989