An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy

Stoean, Ruxandra and Preuss, Mike and Stoean, Catalin and El-Darzi, Elia and Dumitrescu, D. (2009) An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy. In: Foundations of computational intelligence. Studies in computational intelligence, 1 (201). Springer, Berlin Heidelberg, pp. 83-114. ISBN 9783642010811

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Official URL: http://dx.doi.org/10.1007/978-3-642-01082-8_4

Abstract

Support vector machines represent a state-of-the-art paradigm, which has nevertheless been tackled by a number of other approaches in view of the development of a superior hybridized technique. It is also the proposal of present chapter to bring support vector machines together with evolutionary computation, with the aim to offer a simplified solving version for the central optimization problem of determining the equation of the hyperplane deriving from support vector learning. The evolutionary approach suggested in this chapter resolves the complexity of the optimizer, opens the ‘blackbox’ of support vector training and breaks the limits of the canonical solving component.

Item Type: Book Section
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 10 Nov 2010 15:29
Last Modified: 10 Nov 2010 15:29
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/8830

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