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Support vector classification with parameter tuning assisted by agent-based technique
- Source :
-
Computers & Chemical Engineering . Mar2004, Vol. 28 Issue 3, p311. 8p. - Publication Year :
- 2004
-
Abstract
- 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. [Copyright &y& Elsevier]
- Subjects :
- *QUASIANALYTIC functions
*ENGINEERING
*ALGORITHMS
*MATHEMATICAL optimization
Subjects
Details
- Language :
- English
- ISSN :
- 00981354
- Volume :
- 28
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Computers & Chemical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 11729843
- Full Text :
- https://doi.org/10.1016/S0098-1354(03)00188-1