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.

UR - http://www.scopus.com/inward/record.url?scp=23044525372&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=23044525372&partnerID=8YFLogxK

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

Y2 - 21 August 2001 through 25 August 2001

ER -