21. A method for training a classifier comprising: automatically mining an input dataset of text sequences in an unsupervised mode to extract a set of frequent patterns in the text sequences, without regard to class labels of the text sequences; identifying features from among the frequent patterns by filtering out non-relevant and redundant patterns; and applying at least one criterion for determining whether a plurality of the features are able to be merged and if the at least one criterion is met, merging those features, the criterion merging two features F 2 and F 1 into a feature F 1 F 2 if the feature F 1 F 2 is a Markov blanket for the two features; determining a correlation between the class labels and the features identified by the merged feature F 1 F 2 ; and inputting the features, optionally after merging of features, into the classifier; and training the classifier based on the input features and class labels assigned to the corresponding text sequences.
21. A method for training a classifier comprising: automatically mining an input dataset of text sequences in an unsupervised mode to extract a set of frequent patterns in the text sequences, without regard to class labels of the text sequences; identifying features from among the frequent patterns by filtering out non-relevant and redundant patterns; and applying at least one criterion for determining whether a plurality of the features are able to be merged and if the at least one criterion is met, merging those features, the criterion merging two features F 2 and F 1 into a feature F 1 F 2 if the feature F 1 F 2 is a Markov blanket for the two features; determining a correlation between the class labels and the features identified by the merged feature F 1 F 2 ; and inputting the features, optionally after merging of features, into the classifier; and training the classifier based on the input features and class labels assigned to the corresponding text sequences. 24. The method of claim 21 , wherein the filtering out redundant patterns includes determining a symmetrical uncertainty for each of the features and their class label and a symmetrical uncertainty for their disjunction and the class label.