Nonparametric Bayesian methods for supervised and unsupervised learning

I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it can learn relationships like "having a flu causes coughing", while also learning that observable quantities can be usefully grouped into categories like diseases and symptoms, and that diseases tend to cause symptoms, not the other way around. The second method is an online algorithm for learning a prototype-based model for categorial concepts, and can be used to solve problems of multiclass classification with missing features. I apply it to problems of categorizing newsgroup posts and recognizing handwritten digits. These approaches were inspired by a striking capacity of human learning, which should also be a desideratum for any intelligent system: the ability to learn certain kinds of "simple" or "natural" structures very quickly, while still being able to learn arbitrary -- and arbitrarily complex - structures given enough data. In each case, I show how nonparametric Bayesian modeling and inference based on stochastic simulation give us some of the tools we need to achieve this goal.

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

2009

Author dc:creator

• Mansinghka, Vikash Kumar

Advisor dc:contributor.advisor

• Joshua B. Tenenbaum.

• 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/1721.1/53172

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/53172