Date: Monday, January 22, 2001
 Time: 12:00-1:30 (Pizza-while it lasts!)
Place: 1507 Newell-Simon Hall

Title: The Maximum-Margin Approach to Learning Text Classifiers
       Methods, Theory, and Algorithms

Speaker: Thorsten Joachims, post-doc, GMC

Abstract:

Text classification, or the task of automatically assigning semantic
categories to natural language text, has become one of the key methods
for organizing online information. Since hand-coding such
classification rules is costly or even impractical, seemingly every
machine learning algorithm ever invented has been applied to the
problem of learning text classifiers from examples - many with success
on some tasks.  This has led to a wealth of empirical results, but no
theory that explains the results. Nor was any conventional method
found to be both efficient and effective without additional, difficult
to control heuristics. Based on ideas from Support Vector Machines
(SVMs), my dissertation presents the first approach to learning text
classifiers that is highly effective without heuristic components,
that is computationally efficient, and that comes with a learning
theory operational enough to guide applications. In this talk I will
give an overview of the approach, summarizing results on support
vector methods for induction and transduction, efficient performance
estimation, model and parameter selection, and large-scale training
algorithms for SVMs. In more detail I will present the statistical
learning model of text classification with SVMs. It formalizes how
SVMs suit the statistical properties of text, leading to constructive
and intuitive bounds on the expected generalization performance.

Bio:

Thorsten Joachims is a research scientist in the Knowledge Discovery
Group at the National German Research Center for Computer Science
(GMD) in Bonn. He is finishing his dissertation with the title "The
Maximum Margin Approach to Learning Text Classifiers: Methods, Theory,
and Algorithms", advised by Prof. Katharina Morik. He received his
Diploma in Computer Science from the University of Dortmund in 1997
with a thesis on WebWatcher, a browsing assistant for the Web. His
research interests center on a synthesis of theory and system building
in the field of machine learning, with a focus on Support Vector
Machines and machine learning with text. He has authored the SVM-Light
algorithm and software for support vector learning. He co-organized a
AAAI98 workshop on text classification and chaired the IJCAI99
workshop "Machine Learning for Information Filtering". From 1994 to
1996 he was a visiting scientist at Carnegie Mellon University with
Prof. Tom Mitchell. In 1991 he won in the national German Computer
Science competition "Bundeswettbewerb Informatik".

http://ais.gmd.de/~thorsten