Abstract: Aspect of the exemplary embodiment relate to a method and apparatus for automatically identifying features that are suitable for use by a classifier in assigning class labels to text sequences extracted from noisy documents. The exemplary method includes receiving a dataset of text sequences, automatically identifying a set of patterns in the text sequences, and filtering the patterns to generate a set of features. The filtering includes at least one of filtering out redundant patterns and filtering out irrelevant patterns. The method further includes outputting at least some of the features in the set of features, optionally after fusing features which are determined not to affect the classifiers accuracy if they are merged.

Abstract: A clustering system includes a visual mapping sub-system configured to display an N-dimensional to two- or three-dimensional mapping of items to be clustered, where N is greater than three, the mapping having mapping parameters for the N-dimensions. A user interface sub-system is configured to receive user inputted values for the mapping parameters, user inputted values selecting whether selected mapping parameters are fixed or adjustable, and user inputted values associating selected items with selected groups. An adjustment sub-system is configured to adjust the adjustable mapping parameters, without adjusting any fixed mapping parameters, to improve a measure of distinctness of one or more groups of items in the two- or three-dimensional mapping.

Abstract: In a feature filtering approach, a set of relevant features and a set of training objects classified respective to a set of classes are provided. A candidate feature and a second feature are selected from the set of relevant features. An approximate Markov blanket criterion is computed that is indicative of whether the candidate feature is redundant in view of the second feature. The approximate Markov blanket criterion includes at least one dependency on less than the entire set of classes. An optimized set of relevant features is defined, consisting of a sub-set of the set of relevant features from which features indicated as redundant by the selecting and computing are removed.

Abstract: Aspect of the exemplary embodiment relate to a method and apparatus for automatically identifying features that are suitable for use by a classifier in assigning class labels to text sequences extracted from noisy documents. The exemplary method includes receiving a dataset of text sequences, automatically identifying a set of patterns in the text sequences, and filtering the patterns to generate a set of features. The filtering includes at least one of filtering out redundant patterns and filtering out irrelevant patterns. The method further includes outputting at least some of the features in the set of features, optionally after fusing features which are determined not to affect the classifiers accuracy if they are merged.

Abstract: In a feature filtering approach, a set of relevant features and a set of training objects classified respective to a set of classes are provided. A candidate feature and a second feature are selected from the set of relevant features. An approximate Markov blanket criterion is computed that is indicative of whether the candidate feature is redundant in view of the second feature. The approximate Markov blanket criterion includes at least one dependency on less than the entire set of classes. An optimized set of relevant features is defined, consisting of a sub-set of the set of relevant features from which features indicated as redundant by the selecting and computing are removed.

Abstract: A clustering system includes a visual mapping sub-system configured to display an N-dimensional to two- or three-dimensional mapping of items to be clustered, where N is greater than three, the mapping having mapping parameters for the N-dimensions. A user interface sub-system is configured to receive user inputted values for the mapping parameters, user inputted values selecting whether selected mapping parameters are fixed or adjustable, and user inputted values associating selected items with selected groups. An adjustment sub-system is configured to adjust the adjustable mapping parameters, without adjusting any fixed mapping parameters, to improve a measure of distinctness of one or more groups of items in the two- or three-dimensional mapping.