Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A practical Bayesian framework for backpropagation networks
Neural Computation
Machine Learning
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Artificial Neural Networks: Approximation and Learning Theory
Artificial Neural Networks: Approximation and Learning Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
Experimental Analysis of Exchange Ratio in Exchange Monte Carlo Method
Neural Information Processing
Algebraic geometric study of exchange Monte Carlo method
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Continuous speech recognition using linear dynamic models
International Journal of Speech Technology
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We propose a new Bayesian neural network classifier, different from that commonly used in several respects, including the likelihood function, prior specification, and network structure. Under regularity conditions, we show that the decision boundary determined by the new classifier will converge to the true one. We also propose a systematic implementation for the new classifier. In our implementation, the tune of connection weights, the selection of hidden units, and the selection of input variables are unified by sampling from the joint posterior distribution of the network structure and connection weights. The numerical results show that the new classifier consistently outperforms the commonly used Bayesian neural network classifier and the support vector machine in terms of generalization performance. The reason for the inferiority of the commonly used Bayesian neural network classifier and the support vector machine is discussed at length.