Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Multilayer neural networks and Bayes decision theory
Neural Networks
Neural Computation
Neural Computation
Learning of Bayesian Discriminant Functions by a Layered Neural Network
Neural Information Processing
Bayesian decision theory on three-layer neural networks
Neurocomputing
Bayesian learning of neural networks adapted to changes of prior probabilities
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Discriminant analysis by a neural network with mahalanobis distance
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A new algorithm for learning mahalanobis discriminant functions by a neural network
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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We realize a multicategory Bayesian classifier by a three-layer neural network having rather a small number of hidden layer units. The state-conditional probability distributions are supposed to be multivariate normal distributions. The network has direct connections between the input and output layers. Its outputs are monotone mappings of posterior probabilities. Hence, they can be used as discriminant functions and, in addition, the posterior probabilities can be easily retrieved from the outputs.