Enhanced local support vector machine with fast cross-validation capability

Yu Ann Chen, Pau Choo Chung

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

Abstract

Local SVM is a lazy learner combining k-nearest neighbor search and support vector machine classifier. We propose an improved implementation of local SVM which utilizes tree structure for efficient nearest neighbor search and a method to avoid unnecessary SVM training in areas far from decision boundary. The proposed lazy learner has great advantage on cross-validation efficiency while maintaining comparable accuracy to traditional SVM. The proposed method also enables us to conduct leave-one-out cross-validation which is previously considered too time-consuming to be practical on large dataset.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
EditorsWilliam Cheng-Chung Chu, Stephen Jenn-Hwa Yang, Han-Chieh Chao
PublisherIOS Press
Pages491-500
Number of pages10
ISBN (Electronic)9781614994831
DOIs
Publication statusPublished - 2015 Jan 1
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 2014 Dec 122014 Dec 14

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume274
ISSN (Print)0922-6389

Other

OtherInternational Computer Symposium, ICS 2014
CountryTaiwan
CityTaichung
Period14-12-1214-12-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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