Improving the Performance of PieceWise Linear Separation Incremental Algorithms for Practical Hardware Implementations

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dc.creatorDe Lara, Alejandro Chinea Manrique
dc.creatorMoreno, Juan Manuel
dc.creatorMadrenas, Arostegui Jordi
dc.creatorCabestany, Joan
dc.date2007-12-21
dc.date.accessioned2026-07-07T08:50:50Z
dc.date.available2026-07-07T08:50:50Z
dc.descriptionIn this paper we shall review the common problems associated with Piecewise Linear Separation incremental algorithms. This kind of neural models yield poor performances when dealing with some classification problems, due to the evolving schemes used to construct the resulting networks. So as to avoid this undesirable behavior we shall propose a modification criterion. It is based upon the definition of a function which will provide information about the quality of the network growth process during the learning phase. This function is evaluated periodically as the network structure evolves, and will permit, as we shall show through exhaustive benchmarks, to considerably improve the performance(measured in terms of network complexity and generalization capabilities) offered by the networks generated by these incremental models.
dc.description10 pages, 1 figure, 3 tables
dc.identifierhttps://arxiv.org/abs/0712.3654
dc.identifierhttp://arxiv.org/abs/0712.3654
dc.identifierBiological and Artificial Computation: From Neuroscience to Technology, J.Mira, R.Moreno-Diaz, J.Cabestany (eds.), pp. 607-616, Springer-Verlag, 1997
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/144735
dc.subjectNeural and Evolutionary Computing
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.titleImproving the Performance of PieceWise Linear Separation Incremental Algorithms for Practical Hardware Implementations
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