Autori: Ruxandra Stoean, Mike Preuss, Catalin Stoean, Elia El-Darzi, D. Dumitrescu
Editorial: Journal of the Operational Research Society, 60(8), p.1116-1122, 2009.
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 generalise 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.
Cuvinte cheie: support vector machines, evolutionary algorithms