Maximizing mutual information between observations and hidden states to minimize classification errors

The present invention relates to a system and methodology to facilitate machine learning and predictive capabilities in a processing environment. In one aspect of the present invention, a Mutual Information Model is provided to facilitate predictive state determinations in accordance with signal or data analysis, and to mitigate classification error. The model parameters are computed by maximizing a convex combination of the mutual information between hidden states and the observations and the joint likelihood of states and observations in training data. Once the model parameters have been learned, new data can be accurately classified.