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A parametric procedure for imperfectly supervised learning with unknown class probabilities (Corresp.)

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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.

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Information Theory, IEEE Transactions on  (Volume:20 ,  Issue: 5 )