Title :
Determination of the number of hidden units from a statistical viewpoint
Author :
Ayasaka, Taichi ; Agiwara, Katsuyukhi ; Toda, Naohiro ; Usui, Shiro
Author_Institution :
Dept. of Inf. & Comput. Sci., Toyohashi Univ. of Technol., Japan
Abstract :
One of the important problems for 3-layered neural networks (3-LNN) is to determine the optimal network structure with high generalization ability. Although this can be formulated in terms of a statistical model selection, there remains a problem in applying traditional criteria for 3-LNN. We suggest the type of effective criteria for the model selection problem of 3-LNN by analyzing the statistical properties of some simplified nonlinear models. Results of numerical experiments are also presented
Keywords :
generalisation (artificial intelligence); multilayer perceptrons; statistical analysis; generalization; hidden unit determination; nonlinear models; numerical experiments; optimal network structure; regression; statistical model selection; statistical properties; three-layered neural networks; Artificial neural networks; Computer networks; Error analysis; Gaussian distribution; Information science; Neural networks; Nominations and elections; Parameter estimation; Physics computing; Polynomials;
Conference_Titel :
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-5871-6
DOI :
10.1109/ICONIP.1999.843993
