Abstract
We construct a one-hidden-layer neural network capable of learning simultaneously several Bayesian discriminant functions and converting them to the corresponding Mahalanobis discriminant functions in the two-category, normal-distribution case. The Bayesian discriminant functions correspond to the respective situations on which the priors and means depend. The algorithm is characterized by the use of the inner potential of the output unit and additional several memory nodes. It is remarkably simpler when compared with our previous algorithm.
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Ito, Y., Izumi, H., Srinivasan, C. (2012). Simultaneous Learning of Several Bayesian and Mahalanobis Discriminant Functions by a Neural Network with Memory Nodes. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_4
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DOI: https://doi.org/10.1007/978-3-642-34500-5_4
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