Comite de maquinas : uma abordagem unificada empregando maquinas de vetores-suporte / Committee machines: a unified approach using support vector machines

AUTOR(ES)
DATA DE PUBLICAÇÃO

2004

RESUMO

Algorithms based on kernel methods are prominent techniques among the available approaches for machine learning. They were initially applied to implement support vector machines (SVMs). The SVM approach represents a nonparametric learning procedure devoted to high performance classification and regression tasks. However, structural and parametric aspects of the design may guide to performance degradation. In the absence of a systematic and low-cost methodology for the proposition of optimally specified computational models, committee machines emerge as promising alternatives. There exist static versions of committees, in the form of ensembles of components, and dynamic versions, in the form of mixtures of experts. In the present investigation, the components of an ensemble and the experts of a mixture are taken as SVMs. The aim is to jointly explore the potentialities of both SVM and committee machine, by means of a unified formulation. Several extensions and new configurations of committee machines are proposed, with comparative analyses that indicate significant gain in performance before other proposals for machine learning commonly adopted for classification and regression

ASSUNTO(S)

funções de teoria da aproximação artificial intelligence machine learning kernel inteligencia artificial approximation theory kernel functions expert systems (computer science) model theory teoria de modelo redes neurais (computação) aprendizado do computador sistemas especialistas (computação) neural networks (computer science)

ACESSO AO ARTIGO

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