题目： Learning from Noisy Side Information by Generalized Maximum Entropy Model 报告人：Prof. Rong Jin
Department of Computer and Science Engineering
Michigan State University 时间：6月11日 10:30-11:30
地点：蒙民伟楼404会议室 摘要: We consider the problem of learning from noisy side information in the
form of pairwise constraints. Although many algorithms have been developed to
learn from pairwise constraints, most of them assume perfect pairwise
constraints. Given the pairwise constraints are often extracted from data
sources such as paper citations, they tend to be noisy and inaccurate. In this
paper, we introduce the generalization of maximum entropy model and propose a
framework for learning from noisy side information based on the generalized
maximum entropy model. The theoretic analysis shows that under certain
assumption, the classification model trained from the noisy side information
can be very close to the one trained from the perfect side information.
Extensive empirical studies verify the effectiveness of the proposed framework
.
简介: Dr. Rong Jin is an Associate Professor in the Department of Computer and
Science Engineering at Michigan State University. His research is focused on
statistical machine learning and its application to information retrieval, and
has published over 120 conference and journal articles on related topics. Dr.
Jin holds a B.A. in Engineering from Tianjin University, an M.S. in Physics
from Beijing University, and an M.S. and Ph.D. in Computer Science from
Carnegie Mellon University. He joined the Dept. of Computer and Science
Engineering at MSU since 2003, and received the NSF Career Award in 2006.
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