Título: Redes Neurais Construtivas: Uma Abordagem Comparativa
Autores: Castro, Leandro Nunes de; Iyoda, Eduardo Masato; Pinheiro, Eurípedes; Von Zuben, Fernando
Resumo: Until recently, the determination of a proper dimension for an artificial neural network in a given application task usually involved only the designer’s experience in implementing trial and error procedures. Nowadays, automatic design of neural network architectures is becoming part of the training process, by means of a more efficient exploration of the available information for supervised learning. Among the already proposed alternatives to automatic design, this paper emphasizes methods founded on the constructive paradigm. We chose three constructive algorithms for comparison: A* heuristic search (A*), CascadeCorrelation with different activation functions (CASCOR) and Projection Pursuit Learning (PPL). A brief review of these constructive strategies in the solution of regression problems is presented, and their performance in terms of the parsimony of the resultant architecture is verified in benchmark problems.
Artigo em pdf: 4cbrn_025.pdf
Arquivo BibTex: 4cbrn_025.bib