A Multi-objective Learning Algorithm for RBF Neural Network

In this paper, the problem of multi-objective supervised learning is discussed within the non-evolutionary optimization framework. The proposed MOBJ learning algorithm performs the search of Pareto-optimal models determining weights,width, prototype vectors, and the quantity of basis functions of the RBF network. In combination with the Akaike information criterion, the algorithm provides high quality solutions.

[1]  Mee Young Park,et al.  L1‐regularization path algorithm for generalized linear models , 2007 .

[2]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[3]  M. Powell,et al.  Radial basis function interpolation on an infinite regular grid , 1990 .

[4]  Lutz Prechelt,et al.  A Set of Neural Network Benchmark Problems and Benchmarking Rules , 1994 .

[5]  Yaochu Jin,et al.  Multi-Objective Machine Learning , 2006, Studies in Computational Intelligence.

[6]  J. Davenport.  Editor , 1960 .

[7]  K. Lang,et al.  Learning to tell two spirals apart , 1988 .

[8]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[9]  Ricardo H. C. Takahashi,et al.  Multi-Objective Algorithms for Neural Networks Learning , 2006, Multi-Objective Machine Learning.

[10]  V. Kadirkamanathan,et al.  Learning with multi-objective criteria , 1995 .

[11]  Bernhard Sendhoff,et al.  Neural network regularization and ensembling using multi-objective evolutionary algorithms , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[13]  Guo-Ping Liu,et al.  Multiobjective Optimisation And Control , 2008 .

[14]  H. Akaike.  A new look at the statistical model identification , 1974 .

[15]  Antônio de Pádua Braga,et al.  A multi-objective approach to RBF network learning , 2008, Neurocomputing.

[16]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[17]  Ricardo H. C. Takahashi,et al.  Improving generalization of MLPs with multi-objective optimization , 2000, Neurocomputing.

[18]  Antônio de Pádua Braga,et al.  Training neural networks with a multi-objective sliding mode control algorithm , 2003, Neurocomputing.

[19]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.