Statistical Learning Theory and Support Vector Machines

Author

Nasien, Dewi ; Yuhaniz, Siti S. ; Haron, Habibollah

Author_Institution

Soft Comput. Res. Group, Univ. Teknol. Malaysia, Johor Bahru, Malaysia

fYear

2010

fDate

7-10 May 2010

Firstpage

760

Lastpage

764

Abstract

It has been more than 30 years that statistical learning theory (SLT) has been introduced in the field of machine learning. Its objective is to provide a framework for studying the problem of inference that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Support Vector Machine, a method based on SLT, then emerged and becoming a widely accepted method for solving real-world problems. This paper overviews the pattern recognition techniques and describes the state of art in SVM in the field of pattern recognition.

Keywords

inference mechanisms; learning (artificial intelligence); pattern recognition; statistical analysis; support vector machines; SVM; decision making; inference problem; machine learning; pattern recognition; statistical learning theory; support vector machine; Artificial neural networks; Biological neural networks; Machine learning; Neurons; Pattern matching; Pattern recognition; Signal processing; Statistical learning; Support vector machine classification; Support vector machines; Pattern Recognition; Statistical Learning; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Research and Development, 2010 Second International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-0-7695-4043-6

Type

conf

DOI

10.1109/ICCRD.2010.183

Filename

5489503