WestminsterResearch

Support vector machine learning with an evolutionary engine

Stoean, R. and Preuss, M. and Stoean, C. and El-Darzi, Elia and Dumitrescu, D. (2009) Support vector machine learning with an evolutionary engine. Journal of the Operational Research Society, 60 (8). pp. 1116-1122. ISSN 0160-5682

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Official URL: http://dx.doi.org/10.1057/jors.2008.124

Abstract

The paper presents a novel evolutionary technique constructed as an alternative of the standard support vector machines architecture. The approach adopts the learning strategy of the latter but aims to simplify and generalize its training, by offering a transparent substitute to the initial black-box. Contrary to the canonical technique, the evolutionary approach can at all times explicitly acquire the coefficients of the decision function, without any further constraints. Moreover, in order to converge, the evolutionary method does not require the positive (semi-)definition properties for kernels within nonlinear learning. Several potential structures, enhancements and additions are proposed, tested and confirmed using available benchmarking test problems. Computational results show the validity of the new approach in terms of runtime, prediction accuracy and flexibility.

Item Type:Article
Additional Information:Special Issue: Data Mining and Operational Research: Techniques and Applications
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:8827
Deposited On:10 Nov 2010 12:57
Last Modified:10 Nov 2010 12:57

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