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

Stoean, R., Preuss, M., Stoean, C., El-Darzi, E. and Dumitrescu, D. 2009. An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy. in: Abraham, A., Hassanien, A.E., Patrick, S. and Andries, E. (ed.) Foundations of computational intelligence Berlin Heidelberg Springer.

Chapter titleAn evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy
AuthorsStoean, R., Preuss, M., Stoean, C., El-Darzi, E. and Dumitrescu, D.
EditorsAbraham, A., Hassanien, A.E., Patrick, S. and Andries, E.
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.

Book titleFoundations of computational intelligence
Year2009
PublisherSpringer
Publication dates
Published2009
Place of publicationBerlin Heidelberg
SeriesStudies in computational intelligence
ISBN9783642010859
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-642-01085-9_11
Journal citation3 (203), pp. 315-346

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