Título Combined projection and kernel basis functions for classification in evolutionary neural networks
Autores Gutiérrez P.A., Hervás C., CARBONERO RUZ, MARIANO, Fernández J.C., CARBONERO RUZ, MARIANO
Publicación externa No
Medio Adv. Soft Comput.
Alcance Conference Paper
Naturaleza Científica
Cuartil SJR 4
Impacto SJR 0.12900
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-61349197621&doi=10.1007%2f978-3-540-74972-1_13&partnerID=40&md5=c440a0f42c3e0147741262c44414e48e
Fecha de publicacion 01/01/2007
Scopus Id 2-s2.0-61349197621
DOI 10.1007/978-3-540-74972-1_13
Abstract This paper describes a methodology for constructing the hidden layer of a feed forward network using a possible combination of different transfer projection functions (sigmoidal, product) and kernel functions (radial basis functions), where the architecture, weights and node typology is learnt using an evolutionary programming algorithm. The methodology proposed is tested using five benchmark classification problems from well-known machine intelligence problems. We conclude that combined functions are better than pure basis functions for the classification task in several datasets and that the combination of basis functions produces the best models in some other datasets. © 2007 Springer-Verlag Berlin Heidelberg.
Palabras clave Classification; Evolutionary neural networks; Kernel basis functions; Projection basis functions
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