题目： A Potential-based Framework for Multi-class Learning with Partial Feedback 报告人：Prof. Rong Jin
Department of Computer and Science Engineering
Michigan State University 时间：May 23 10:30-11:30 地点：蒙民伟楼404会议室 摘要: We study the problem of online multi-class learning with partial
feedback: in each trial of online learning, instead of providing the true
class label for a given instance, the oracle will only reveal to the learner
if the predicted class label is correct. We present a general framework for
online multi-class learning with partial feedback that adapts the potential-
based gradient descent approaches ~\cite{bianchi-2006-prediction}. The
generality of the proposed framework is verified by the fact that Banditron is
indeed a special case of our work. We propose an exponential gradient
algorithm for online multi-class learning with partial feedback. Compared to
the Banditron algorithm, the exponential gradient algorithm is advantageous in
that its mistake bound is independent from the dimension of data, making it
suitable for classifying high dimensional data. We also investigate the
problem of how to determine the optimal tradeoff between exploration and
exploitation in multi-class learning with partial feedback. Our empirical
study with text categorization, optimal letter recognition, and ad selection
shows promising results of the proposed methods. 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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