1. A method for automated selection of a parameterized operator sequence to generate a pattern classification task, comprising the steps of: inputting a collection of labeled data patterns, wherein the labeled data patterns include images labeled as an object of interest and a background of the object of interest; deriving statistical descriptions of the inputted labeled data patterns, wherein the statistical descriptions include Hidden Markov Model parameters; determining classifier performance for each of a plurality of candidate operator sequences and corresponding parameter values, using the derived statistical descriptions; identifying an optimal classifier performance among the determined classifier performances based on probabilities computed by an Embedded Markov Model Chain; and selecting the operator sequence and corresponding parameter values, associated with the identified optimal classifier performance, wherein the method is performed by a processor.