Sequential structure and parameter-adaptive pattern recognition--I: Supervised learning
- 1 September 1970
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 16 (5), 548-556
- https://doi.org/10.1109/tit.1970.1054533
Abstract
Bayes optimal sequential structure and parameter-adaptive pattern-recognition systems for continuous data are derived. Both off-line (or prior to actual operation) and on-line (while in operation) supervised learning is considered. The concept of structure adaptation is introduced and both structure as well as parameter-adaptive optimal pattern-recognition systems are obtained. Specifically, for the class of supervised-learning pattern-recognition problems with Gaussian process models and linear dynamics, the adaptive pattern-recognition systems are shown to be decomposable ("partition theorem") into a linear nonadaptive part consisting of recursive matched Kalman filters, a nonlinear part--a set of probability computers--that incorporates the adaptive nature of the system, and finally a part of the correlator-estimator (Kailath) form. Extensions of the above results to theM-ary hypotheses case whereM \geq 2are given.Keywords
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