Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well
in many real world applications, despite the strong assumption that all features are …

Quantitative attributes are usually discretized in Naive-Bayes learning. We establish simple
conditions under which discretization is equivalent to use of the true probability density …

Although machine learning is becoming commonly used in today's software, there has been
little research into how end users might interact with machine learning systems, beyond …

We present a neural network based system for automated e-mail filing into folders and anti-
spam filtering. The experiments show that it is more accurate than several other techniques …

With the increase in information on the Internet, the strive to find more effective tools for
distinguishing between interesting and non-interesting material is increasing. Drawing …

Modern email clients support predicate-based folder assignment rules that can automatically
organize emails. Unfortunately, users still need to write these rules manually. Prior machine …

This paper addresses the problem of free riding on peer-to-peer resource-sharing networks
and explores methods for motivating more cooperative user behaviour via an adaptive …

Machine learning algorithms hold extraordinary promise, but the reality is that their success
depends entirely on the suitability of the data available. This book is about Ripple-Down …

The potential for machine learning systems to improve via a mutually beneficial exchange of
information with users has yet to be explored in much detail. Previously, we found that users …

Email has met tremendous popularity over the past few years. People are sending and
receiving many messages per day, communicating with partners and friends, or exchanging …