Clustered Naive Bayes

Humans effortlessly use experience from related tasks to improve their performance at novel tasks. In machine learning, we are often confronted with data from "related" tasks and asked to make predictions for a new task. How can we use the related data to make the best prediction possible? In this thesis, I present the Clustered Naive Bayes classifier, a hierarchical extension of the classic Naive Bayes classifier that ties several distinct Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters. A priori, the model assumes that there exists a partitioning of the data sets such that, within each subset, the data sets are identically distributed. I evaluate the resulting model in a meeting domain, developing a system that automatically responds to meeting requests, partially taking on the responsibilities of a human office assistant. The system decides, based on a learned model of the user's behavior, whether to accept or reject the request on his or her behalf. The extended model outperforms the standard Naive Bayes model by using data from other users to influence its predictions.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Roy, Daniel Murphy

Advisor dc:contributor.advisor

• Leslie Pack Kaelbling.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/37075

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/37075