1. A method, comprising: a computer system processing each text sample in a training set, wherein each text sample in the training set corresponds to one of a plurality of classifications, and wherein processing each text sample includes: generating a respective set of features from that text sample; populating an entry in a data structure based on results of one or more dimension reduction operations performed on ones of the respective set of features for that text sample; the computer system using a non-linear classifier on entries in the data structure to establish criteria usable to classify an unknown text sample into one of the plurality of classifications; wherein each text sample in the training set is made up of characters within a text character set having C characters, wherein each feature in the respective set of features for a given text sample in the training set has N characters, and wherein populating an entry in the data structure for the given text sample includes a dimension reduction operation on individual ones of the respective set of features such that a corresponding value for each of those features is reduced from C N possible values to not greater than C N-1 possible values; wherein N is an integer greater than or equal to 3 and C is an integer greater than or equal to 20.
1. A method, comprising: a computer system processing each text sample in a training set, wherein each text sample in the training set corresponds to one of a plurality of classifications, and wherein processing each text sample includes: generating a respective set of features from that text sample; populating an entry in a data structure based on results of one or more dimension reduction operations performed on ones of the respective set of features for that text sample; the computer system using a non-linear classifier on entries in the data structure to establish criteria usable to classify an unknown text sample into one of the plurality of classifications; wherein each text sample in the training set is made up of characters within a text character set having C characters, wherein each feature in the respective set of features for a given text sample in the training set has N characters, and wherein populating an entry in the data structure for the given text sample includes a dimension reduction operation on individual ones of the respective set of features such that a corresponding value for each of those features is reduced from C N possible values to not greater than C N-1 possible values; wherein N is an integer greater than or equal to 3 and C is an integer greater than or equal to 20. 9. The method of claim 1 , wherein populating an entry in the data structure for a given text sample comprises: hashing one of the respective set of features for the given text sample by performing a plurality of hashing operations; and modifying a value of a selected one of a plurality of containers in the entry based on the hashing.