The Q-norm complexity measure and the minimum gradient method: a novel approach to the machine learning structural risk minimization problem.
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.
Vieira
D A G
DA
Department of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, MG 31270-010, Brazil. douglas@cpdee.ufmg.br
Takahashi
Ricardo H C
RH
Palade
Vasile
V
Vasconcelos
J A
JA
Caminhas
W M
WM
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*