An architecture of active learning SVMs for spam
Kunlun Li, Huang Houkuan · 2003
We propose a new method for spam categorization based on support vector machines (SVMs) using active learning strategy. We study the use of support vector machines in classifying e-mail as spam or nonspam. It analyzes the particular properties of our special task and identifies why SVMs are appropriate for dealing with spam. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new method for choosing which instances to request next.