The Q-norm complexity measure and the minimum gradient method: a novel approach to the machine learning structural risk minimization problem.
IEEE Trans Neural Netw
; 19(8): 1415-30, 2008 Aug.
Article
em En
| MEDLINE
| ID: mdl-18701371
This paper presents a novel approach for dealing with the structural risk minimization (SRM) applied to a general setting of the machine learning problem. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem in which two conflicting objectives should be minimized. The objectives are related to the empirical training error and the machine complexity. In this paper, one general Q-norm method to compute the machine complexity is presented, and, as a particular practical case, the minimum gradient method (MGM) is derived relying on the definition of the fat-shattering dimension. A practical mechanism for parallel layer perceptron (PLP) network training, involving only quasi-convex functions, is generated using the aforementioned definitions. Experimental results on 15 different benchmarks are presented, which show the potential of the proposed ideas.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Modelos Teóricos
Tipo de estudo:
Etiology_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Neural Netw
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos