A learning algorithm for enhancing the generalization ability of support vector machines

Jun Guo, Norikazu Takahashi, Tetsuo Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose an innovative learning algorithm for enhancing the generalization ability of support vector machines (SVMs), when the Gausssian radial basis function (RBF) is used and when the parameter σ is very small. As learning patterns it uses not only the prescribed learning patterns but also newly inserted patterns in their neighbourhoods. In spite of the inserted many patterns, the size of the proposed optimization problem can be reduced to be same as the original one by using the averaging method. Many simulation results show the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Pages3631-3634
Number of pages4
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan
Duration: May 23 2005May 26 2005

Other

OtherIEEE International Symposium on Circuits and Systems 2005, ISCAS 2005
CountryJapan
CityKobe
Period5/23/055/26/05

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Guo, J., Takahashi, N., & Nishi, T. (2005). A learning algorithm for enhancing the generalization ability of support vector machines. In Proceedings - IEEE International Symposium on Circuits and Systems (pp. 3631-3634). [1465416] https://doi.org/10.1109/ISCAS.2005.1465416