An algorithm to cluster data for efficient classification of support vector machines

Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications Vol. 34; no. 3; pp. 2013 - 2018
Main Authors: Li, Der-Chiang, Fang, Yao-Hwei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2008
Subjects:
Computational complexity
Density-based clustering algorithm
Machine learning
Support vector machines
Support vector machines
Density-based clustering algorithm
Computational complexity
Machine learning
ISSN:0957-4174, 1873-6793
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.02.016