A note on learning for Gaussian properties
- 1 January 1965
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 11 (1), 126-132
- https://doi.org/10.1109/tit.1965.1053726
Abstract
By employing a Bayesian approach to the analysis of learning the probability distribution of property vectors, an estimation likelihood computation scheme for the general Gaussian distribution (quadratic adaptive decision surface) is shown optimum. Some results relating the number of learning samples to Type I misclassification errors are included.Keywords
This publication has 4 references indexed in Scilit:
- Pattern recognition and machine learningIEEE Transactions on Information Theory, 1963
- Learning to recognize patterns in a random environmentIEEE Transactions on Information Theory, 1962
- Learning Filters for Optimum Pattern RecognitionIEEE Transactions on Information Theory, 1962
- Recognition of membership in classesIEEE Transactions on Information Theory, 1961