Bayesian Radial Basis Function Neural Network

Bayesian radial basis function neural network is presented to explore the weight structure in radial-basis function neural networks for discriminant analysis. The work is motivated by the empirical experiments where the weights often follow certain probability density functions in protein sequence analysis using the bio-basis function neural network, an extension to radial basis function neural networks. An expectation-maximization learning algorithm is proposed for the estimation of the weights of the proposed Bayesian radial-basis function neural network and the simulation results show that the proposed novel radial basis function neural network performed the best among various algorithms.

[1]  Zheng Rong Yang,et al.  Bio-basis function neural network for prediction of protease cleavage sites in proteins , 2005, IEEE Transactions on Neural Networks.

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

[3]  Kuo-Chen Chou,et al.  Predicting the linkage sites in glycoproteins using bio-basis function neural network , 2004, Bioinform..

[4]  Sheng Chen,et al.  Robust maximum likelihood training of heteroscedastic probabilistic neural networks , 1998, Neural Networks.

[5]  Nando de Freitas,et al.  Robust Full Bayesian Learning for Radial Basis Networks , 2001, Neural Computation.

[6]  Michael E. Tipping.  The Relevance Vector Machine , 1999, NIPS.

[7]  Ruisheng Zhang,et al.  Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks , 2005 .

[8]  Zheng Rong Yang,et al.  Characterizing proteolytic cleavage site activity using bio-basis function neural networks , 2003, Bioinform..

[9]  David E. Rumelhart,et al.  Generalization by Weight-Elimination with Application to Forecasting , 1990, NIPS.

[10]  Juan Julián Merelo Guervós,et al.  Evolving RBF neural networks for time-series forecasting with EvRBF , 2004, Inf. Sci..

[11]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[12]  Kezhi Mao,et al.  RBF neural network center selection based on Fisher ratio class separability measure , 2002, IEEE Trans. Neural Networks.

[13]  Peter L. Bartlett,et al.  For Valid Generalization the Size of the Weights is More Important than the Size of the Network , 1996, NIPS.

[14]  Zhide Hu,et al.  Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous–alcoholic solvents by capillary zone electrophoresis based on support vector machine , 2004 .

[15]  Peter Sykacek,et al.  On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling , 1999, NIPS.

[16]  Lionel Tarassenko,et al.  Estimations of error bounds for neural-network function approximators , 1999, IEEE Trans. Neural Networks.

[17]  Ram Samudrala,et al.  An enhanced Java graph applet interface for visualizing interactomes , 2005, Bioinform..

[18]  Hong Chen,et al.  Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks , 1993, IEEE Trans. Neural Networks.

[19]  Pawel Strumillo,et al.  Kernel orthonormalization in radial basis function neural networks , 1997, IEEE Trans. Neural Networks.

[20]  Panagiotis Patrinos,et al.  A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models , 2005 .

[21]  Witold Pedrycz,et al.  Conditional fuzzy clustering in the design of radial basis function neural networks , 1998, IEEE Trans. Neural Networks.

[22]  James J. Carroll,et al.  Approximation of nonlinear systems with radial basis function neural networks , 2001, IEEE Trans. Neural Networks.

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[24]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[25]  C. C. Homes,et al.  Bayesian Radial Basis Functions of Variable Dimension , 1998, Neural Computation.

[26]  Zheng Rong Yang,et al.  Orthogonal kernel Machine for the prediction of functional sites in proteins , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[27]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .