Learning with probabilistic labeling

摘要

A nonsupervised parametric learning model using a randomized labeling procedure is discussed. Our model is an extension of the Agrawala's model and is applicable even in the case where the probability of occurrence of each category is unknown. Furthermore, the method proposed here is computationally feasible to identify a finite mixture. The learning algorithm for multivariate normal distribution is presented in this paper.