Fordham University Department of Computer & Information
Science
And
The Society of Computer Science
Present
Inference and Learning with Networked Data
by
Professor Foster Provost
Stern School of Business
New York University
Date: | Friday, April 27, 2007; 11:30am |
Location: | John Mulcahy Hall, Room
403 |
Light refreshment provided after the talk |
Abstract:
For many applications we would like to draw inferences about objects
that are interconnected in complex networks. For example, commercial
transactions link consumers into huge social networks. In this talk I
start by introducing various applications of classification in
networked data, from viral marketing to fraud detection to
counter-terrorism. Traditional statistical and machine learning
classification methods assume that objects to be classified or scored
are independent of each other. I then discuss two characteristics of
classification in networked data that differentiate it from
traditional classification, and which can improve classification
tremendously: (i) the opportunity to perform collective inference,
using inferences on linked data to mutually reinforce each other, and
(ii) the ability to use specific identifiers, such as the identities
of particular individuals, to improve inference. I present results
demonstrating the effectiveness of these techniques.
Bio:
Foster Provost is Associate Professor and NEC Faculty Fellow at New
York University's Stern School. He is Editor-in-Chief of the journal
Machine Learning, a founding board member of the International Machine
Learning Society, and was program chair of the ACM SIGKDD Conference
in 2001. He has received Faculty Awards from IBM and a President's
Award from NYNEX Science and Technology. His recent research has
focused on classification in network data and utility-based data
mining. He has been involved in various applications of machine
learning technology to real-world problems, including fraud detection,
network diagnosis, customer contact management, targeted marketing,
and counterterrorism.
For more information, contact:
Ms. Diane Roche (718) 817-4480; (roche@cis.fordham.edu)
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