TITLE: Classification by Conditional Probability Estimation
AFFILIATION: School of Computing and Mathematics, Deakin University
DATE: Friday 12 April 2002
TIME: 12:00 noon - 1:00pm
PLACE: Seminar Room K17
The naive Bayes classifier is a computationally efficient, elegant, and theoretically well-motivated approach to classification learning. Despite its simplicity, it has high classification accuracy, especially for small data sets. This talk presents our research into improving naive Bayes, retaining its efficiency, elegance, and direct theoretical base, while further strengthening its classification accuracy. I present two key techniques, one that delivers very high classification accuracy at some computational cost and the other that delivers substantially improved classification accuracy at modest computational cost.
BIOGRAPHY OF SPEAKER:
Geoff Webb holds a Personal Chair in the School of Computing and Mathematics at Deakin University. He has published over 90 papers in the areas of machine learning, data mining, and user modelling. He has received more than $840,000 in national competitive research grants. He is a member of the editorial boards of User Modelling and User-Adapted Interaction (UMUAI) and Knowledge and Information Systems (KAIS).
School of Computer Science & Engineering, UNSW.