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Kernel Methods for Estimation and Data Analysis
Speaker: Alex Smola
Affiliation: National ICT Australia / ANU
Description: In this course I will discuss modern kernel methods for classification, regression and sequence annotation. The work is based on exponential families and graphical models. Using these tools one can obtain a unified and simple approach to estimation.
In the second part of the tutorial I will discuss how nonparametric methods can be used for unsupervised learning, such as feature selection, independent component analysis, database merging and schema matching, and sample bias correction. The resulting algorithms are extremely simple to implement, yet they are better than the best currently available methods in statistics.
Presenter biography: I studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time I was at the Maximilianeum München and the Collegio Ghislieri in Pavia. In 1996 I received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999 I was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Geselschaft). After that, I worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University.
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