Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

December 1, 2001, Vol. 13, No. 12, Pages 2851-2863
(doi: 10.1162/089976601317098556)
© 2001 Massachusetts Institute of Technology
Linear Constraints on Weight Representation for Generalized Learning of Multilayer Networks
Article PDF (115.28 KB)
Abstract

In this article, we present a technique to improve the generalization ability of multilayer neural networks. The proposed method introduces linear constraints on weight representation based on the invariance natures of training targets. We propose a learning method that introduces effective linear constraints into an error function as a penalty term. Furthermore, introduction of such constraints leads to reduction of the VC dimension of neural networks. We show bounds on the VC dimension of the neural networks with such constraints. Finally, we demonstrate the effectiveness of the proposed method by some experiments.