Discriminant Analysis by a Neural Network with Mahalanobis Distance
We propose a neural network which can approximate Mahalanobis discriminant functions after being trained. It can be realized if a Bayesian neural network is equipped with two additional subnetworks. The training is performed sequentially and, hence, the past teacher signals need not be memorized. In this paper, we treat the two-category normal-distribution case. The results of simple simulations are included.
KeywordsNeural Network Discriminant Analysis Discriminant Function Prior Probability Mahalanobis Distance
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- 3.Ito, Y.: Simultaneous L p -approximations of polynomials and derivatives on Rd and their applications to neural networks (in preparation)Google Scholar
- 4.Ito, Y., Srinivasan, C.: Multicategory Bayesian decision using a three-layer neural network. In: Proceedings of ICANN/ICONIP 2003, pp. 253–261 (2003)Google Scholar
- 6.Ito, Y., Srinivasan, C., Izumi, H.: Bayesian learning of neural networks adapted to changes of prior probabilities. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 253–259. Springer, Heidelberg (2005)Google Scholar