Batch Mode Active Learning

When: Friday October 27, at 10amWhere: Intel Research (4th floor CIC)
Speaker: Rong Jin (MSU)Title: Batch Mode Active Learning

Abstract:
The goal of active learning is to select the most informativeexamples for manual labeling. Most of the previous studies in activelearning have focused on selecting a single unlabeled example in eachiteration. This is inefficient since the classification model has to beretrained for every labeled example that is solicited. In this paper, wepresent a framework for "batch mode active learning" that applies theFisher information matrix to select a number of informative examplessimultaneously. The key computational challenge is how to efficientlyidentify the subset of unlabeled examples that can result in thelargest reduction in the classification uncertainty. In this talk, Iwill discuss two different computational approaches: one is based onthe approximated semi-definitive programming technique and the other isbased on the property of submodular functions. Empirical studies showthe promising results of the proposed approaches for batch mode activelearning in comparison to the state-of-the-art active learning methods.

Bio:
Dr. Rong Jin is an assistant Prof. of the Computer and Science
Engineering Dept. of Michigan State University since 2003. He is working
in the areas of statistical machine learning and its application to
information retrieval. In the past, Dr. Jin has worked on a variety
of machine learning algorithms, and has presented efficient and
robust algorithms for conditional exponential models, support vector
machine, and boosting. In addition, he has extensive experience
with the application of machine learning algorithms to information
retrieval, including retrieval models, collaborative filtering, cross
lingual information retrieval, document clustering, and video/image
retrieval. In the past, he has published over sixty conference and
journal articles on the 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 the area of language technologies
from Carnegie Mellon University.