Supervised learning sequential structure and parameter adaptive pattern recognition: Discrete data case (Corresp.)
DEMETRIOS G. LAINIOTIS · IEEE Transactions on Information Theory · 1971
In a previous paper [1], Bayes-optimal recursive supervised learning structure and parameter adaptive pattern recognition systems were derived for continuous "lumped" Gaussian processes. In this paper, the discrete data case is considered, and the discrete data version of the partition theorem is derived. Several examples are also presented of the application of the adaptive detectors, and computational results are given indicating their learning capacity and convergence rate.