A parametric procedure for imperfectly supervised learning with unknown class probabilities (Corresp.)
- 1 September 1974
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 20 (5) , 661-663
- https://doi.org/10.1109/tit.1974.1055273
Abstract
A computationally feasible parametric procedure for unsupervised learning has been given by Agrawala [1]. The procedure eliminates the computational difficulties associated with updating using a mixture density by making use of a probabilistic labeling scheme. Shanmugam [2] has given a similar parametric procedure using probabilistic labeling for the more general problem of imperfectly supervised learning. Both procedures assume known class probabilities. In this correspondence a computationally feasible parametric procedure using probabilistic labeling is given for imperfectly supervised learning when the class probabilities are among the unknown statistical parameters.Keywords
This publication has 2 references indexed in Scilit:
- A parametric procedure for learning with an imperfect teacher (Corresp.)IEEE Transactions on Information Theory, 1972
- Learning with a probabilistic teacherIEEE Transactions on Information Theory, 1970