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Department of Computer & Information Science And The Society of Computer Science Present | ||||||
| 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)