22. The method of claim 19 further comprising the step of utilizing messages in the first class as the training set.
19. The method in claim 4 further comprises a training phase having the steps of: detecting whether each one of a plurality of predetermined features exists in each message of a training set of m messages belonging to the first class so as to yield a feature matrix containing feature data for all of the training messages, wherein the plurality of predetermined features defines a predefined n-element feature space and each of the training messages has been previously classified as belonging to the first class; reducing the feature matrix in size to yield a reduced feature matrix having said N features (where n, N and m are integers with n&gt;N); and applying the reduced feature matrix and the known classifications of each of said training messages to the classifier and training the classifier to recognize the N features in the m-message training set.