Incremental Learning in SwiftFile

Abstract

SwiftFile is an intelligent assistant that helps users organize their e-mail into folders. SwiftFile uses a text classifier to predict where each new message is likely to be filed by the user and provides shortcut buttons to quickly file messages into one of its predicted folders. One of the challenges faced by SwiftFile is that the user's mail-ling habits are constantly changing -- users are frequently creating, deleting and rearranging folders to meet their current ling needs. In this paper, we discuss the importance of incremental learning in SwiftFile. We present several criteria for judging how well incremental learning algorithms adapt to quickly changing data and evaluate SwiftFile's classifier using these criteria. We find that SwiftFile's classifier is surprisingly responsive and does not require the extensive training that is often assumed in most learning systems.