TY - GEN
T1 - Approximation of Bayesian discriminant function by neural networks in terms of Kullback-Leibler information
AU - Ito, Yoshifusa
AU - Srinivasan, Cidambi
PY - 2001
Y1 - 2001
N2 - Following general arguments on approximation Bayesian discriminant functions by neural networks, rigorously proved is that a three layered neural network, having rather a small number of hidden layer units, can approximate the Bayesian discriminant function for the two category classification if the log ratio of the a posteriori probability is a polynomial. The accuracy of approximation is measured by the Kullback- Leibler information. An extension to the multi-category case is also discussed.
AB - Following general arguments on approximation Bayesian discriminant functions by neural networks, rigorously proved is that a three layered neural network, having rather a small number of hidden layer units, can approximate the Bayesian discriminant function for the two category classification if the log ratio of the a posteriori probability is a polynomial. The accuracy of approximation is measured by the Kullback- Leibler information. An extension to the multi-category case is also discussed.
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U2 - 10.1007/3-540-44668-0_19
DO - 10.1007/3-540-44668-0_19
M3 - Conference contribution
AN - SCOPUS:23044525372
SN - 3540424865
SN - 9783540446682
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 140
BT - Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings
A2 - Hornik, Kurt
A2 - Dorffner, Georg
A2 - Bischof, Horst
T2 - International Conference on Artificial Neural Networks, ICANN 2001
Y2 - 21 August 2001 through 25 August 2001
ER -