PROBABILISTIC RANK AGGREGATION FOR MULTIPLE SVM RANKING

MPhil Thesis Defence


Title: "PROBABILISTIC RANK AGGREGATION FOR
MULTIPLE SVM RANKING"

By

Mr. Chi-Wai Cheung


Abstract

Learning to rank is a fast growing research problem in Machine Learning and 
Information Retrieval. Ranking Support Vector Machine (RSVM) is a widely 
adopted ranking method in various fields because of its good generalization 
performance. RSVM transforms the learning to rank problem into a classification 
problem, and employs a single hyperplane to separate the instances. Recently 
there have been several ranking methods proposed based on RSVM. Those methods 
employ multiple hyperplanes so that a local ranking is produced from each 
hyperplane. Rank aggregation is then conducted to combine the local rankings. 
However, under this process the information from the individual hyperplane is 
not fully utilized. In this thesis, we address the problem of aggregating the 
rankings using the SVM output values and propose a novel rank aggregation 
framework based on a probabilistic view. In this framework we define two rank 
aggregation methods and conducted experiments to show the improvement of 
utilizing the SVM output values.


Date:			Wednesday, 19 August 2009

Time:			3:00pm-5:00pm

Venue:			Room 3501
 			Lifts 25-26

Committee Members:	Prof. Dik-Lun Lee (Supervisor)
 			Dr. Wilfred Ng (Chairperson)
 			Dr. Lei Chen


**** ALL are Welcome ****