In this paper, we have proposed an architecture of active learning SVMs with
relevance feedback (RF)for classifying e-mail. This architecture combines both
active learning strategies where instead of using a randomly selected training
set, the learner has access to a pool of unlabeled instances and can request
the labels of some number of them and relevance feedback where if any mail
misclassified then the next set of support vectors will be different from the
present set otherwise the next set will not change. Our proposed architecture
will ensure that a legitimate e-mail will not be dropped in the event of
overflowing mailbox. The proposed architecture also exhibits dynamic updating
characteristics making life as difficult for the spammer as possible.