1. A method of classifying an incoming electronic message, as a function of content of the message, into one of a plurality of predefined classes, the method comprising the steps of: determining whether each one of a pre-defined set of N features (where N is a predefined integer) is present in the incoming message so as to yield feature data associated with the message; applying the feature data to a probabilistic classifier so as to yield an output confidence level for the incoming message which specifies a probability that the incoming message belongs to said one class, wherein the classifier has been trained, on past classifications of message content for a plurality of messages that form a training set and belong to said one class, to recognize said N features in the training set; classifying, in response to a magnitude of the output confidence level, the incoming message as a member of said one class of messages; automatically updating the training set to include classification of message content for an incoming message which has been classified by a user in another one of the predefined classes other than said one class specified by the classifier so as to form an updated training set; and automatically re-training the classifier based on the updated training set so as to adapt the operation of the classifier to changes in either message content that affect message classification or in user perceptions of the content of incoming messages.
1. A method of classifying an incoming electronic message, as a function of content of the message, into one of a plurality of predefined classes, the method comprising the steps of: determining whether each one of a pre-defined set of N features (where N is a predefined integer) is present in the incoming message so as to yield feature data associated with the message; applying the feature data to a probabilistic classifier so as to yield an output confidence level for the incoming message which specifies a probability that the incoming message belongs to said one class, wherein the classifier has been trained, on past classifications of message content for a plurality of messages that form a training set and belong to said one class, to recognize said N features in the training set; classifying, in response to a magnitude of the output confidence level, the incoming message as a member of said one class of messages; automatically updating the training set to include classification of message content for an incoming message which has been classified by a user in another one of the predefined classes other than said one class specified by the classifier so as to form an updated training set; and automatically re-training the classifier based on the updated training set so as to adapt the operation of the classifier to changes in either message content that affect message classification or in user perceptions of the content of incoming messages. 33. A computer readable medium having computer executable instructions stored therein for performing the steps of claim 1.