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