Neural networks and the bias/variance dilemma
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Bayesian radial basis functions of variable dimension
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Robust Full Bayesian Learning for Radial Basis Networks
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Orthogonal kernel Machine for the prediction of functional sites in proteins
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel orthonormalization in radial basis function neural networks
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Estimations of error bounds for neural-network function approximators
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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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.