M-SVC (mixed-norm SVC) - a novel form of support vector classifier

Author

Lan, Lcu-Shing

Author_Institution

Dept. of Electron. Eng., Nat. Yunlin Univ. of Sci. & Technol.

fYear

2006

fDate

21-24 May 2006

Lastpage

3264

Abstract

Support vector machines are currently a very active research area within machine learning, data mining, and other related research communities. This paper presents a new form of generalized support vector classifier that includes both the 1-norm and 2-norm SVCs as its special cases. We refer to this new SVC as the mixed-norm support vector classifier (or m-SVC). The dual form of the m-SVC optimization problem is explicitly derived. A decomposition-type algorithm is described to solve the large sample-size m-SVC problem. We give some examples to demonstrate the solvability of the m-SVC formulation and to illustrate the relations among the m-SVC and convectional 1-norm and 2-norm SVCs

Keywords

data mining; learning (artificial intelligence); optimisation; support vector machines; 1-norm SVC; 2-norm SVC; data mining; decomposition-type algorithm; machine learning; mixed-norm SVC; mixed-norm support vector classifier; support vector machines; Data mining; Functional analysis; Image analysis; Kernel; Machine learning; Optical character recognition software; Static VAr compensators; Support vector machine classification; Support vector machines; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on

Conference_Location

Island of Kos

Print_ISBN

0-7803-9389-9

Type

conf

DOI

10.1109/ISCAS.2006.1693321

Filename

1693321