Comparing the performance of support vector machines to regression with structural risk minimisation

Author

Viswanathan, Murlikrishna ; Kotagiri, Ramamohanarao

Author_Institution

Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Parkville, Vic., Australia

fYear

2004

fDate

2004

Firstpage

445

Lastpage

449

Abstract

The structural risk minimisation (SRM) principle based on the statistical learning theory of Vapnik aims to prevent the phenomenon of overfitting by balancing the complexity of models with their fit to the data. This principle has been embodied in support vector machines, a widely acclaimed generic approach to machine learning. This paper investigates the performance of the SRM principle in its application to standard least-squares regression and compares it with its integration with support vector machines.

Keywords

learning (artificial intelligence); least squares approximations; minimisation; regression analysis; support vector machines; machine learning; regression analysis; standard least-squares regression; statistical learning theory; structural risk minimisation; support vector machines; Computer errors; Computer science; Machine learning; Minimization methods; Polynomials; Software engineering; Statistical learning; Support vector machine classification; Support vector machines; Virtual colonoscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Sensing and Information Processing, 2004. Proceedings of International Conference on

Print_ISBN

0-7803-8243-9

Type

conf

DOI

10.1109/ICISIP.2004.1287698

Filename

1287698