Header menu link for other important links
Support vector classification with parameter tuning assisted by agent-based technique
A. Kulkarni, , B.D. Kulkarni
Published in Elsevier Ltd
Volume: 28
Issue: 3
Pages: 311 - 318
This paper describes a robust support vector machines (SVMs) classification methodology, which can offer superior classification performance for important process engineering problems. The method incorporates efficient tuning procedures based on minimization of radius/margin and span bound for leave-one-out errors. An agent-based asynchronous teams (A-teams) software framework, which combines Genetic-Quasi-Newton algorithms for the optimization is highly successful in obtaining the optimal SVM hyper-parameters. The algorithm has been applied for classification of binary as well as multi-class real world problems. © 2003 Elsevier Ltd. All rights reserved.
About the journal
Published in Elsevier Ltd
Open Access
Impact factor