1. A method of handling messages in a messaging system that includes a message gateway and individual message boxes for users of the system, wherein a message addressed to a user is delivered to the user's message box after passing through the message gateway, the method comprising: knowingly biasing a global, scoring e-mail classifier relative to a personal, scoring e-mail classifier such that the global, scoring e-mail classifier is less stringent than the personal, scoring e-mail classifier as to what is classified as spam, wherein the global, scoring e-mail classifier and the personal, scoring e-mail classifier are probabilistic e-mail classifiers such that, to classify a message, the global, scoring e-mail classifier and the personal, scoring e-mail classifier use respective internal models to determine a probability measure for the message and compare the probability measure to a classification threshold; receiving messages at the message gateway; inputting the received messages into the global, scoring e-mail classifier to classify the input messages as spam or non-spam; handling at least one of the messages input into the global, scoring e-mail classifier based on whether the global, scoring e-mail classifier classified the at least one message as spam or non-spam; outputting at least one message from the global, scoring e-mail classifier, wherein the outputted message has been classified as non-spam by the global, scoring e-mail classifier; inputting the outputted message from the global, scoring e-mail classifier into the personal, scoring e-mail classifier to classify the at least one outputted message as spam or non-spam; handling the at least one outputted message input into the personal, scoring e-mail classifier based on whether the personal, scoring e-mail classifier classified the at least one outputted message as spam or non-spam; receiving an indication from a user to change the classification of the at least one outputted message; in response to the indication, changing the classification of the at least one outputted message; generating retraining data based on the change to the classification of the at least one outputted message; and retraining the personal, scoring e-mail classifier based on the generated retraining data such that the personal, scoring e-mail classifier's internal model is refined to track the user's subjective perceptions as to what messages constitute spam messages.