 |
|
|
|
|
|
|
|
|
|
|
|
|
 |
|
|||||||||||
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
|
|
 |
 |
|
||||||||||
 |
|
|||||||||||
 |
|
|||||||||||
 |
 |
|
||||||||||
| Seminar Schedule (Seminar Organizer: Prof. Ziv Bar-Joseph) | |
Machine Learning Lunchtime Chats
|
Spring 2006 Seminar
Date: February 15, 2006 Time: 12:00 PM - 1:00 PM (Pizza will be served while it lasts.) Location: 1305 Newell Simon Hall Speaker: Thorsten Joachims, Professor, Department of Computer Science at Cornell University Title: Support Vector Machines for Structured Outputs Abstract: Over the last decade, much of the research on discriminative learning has focused on problems like classification and regression, where the prediction is a single univariate variable. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise, for example, when a natural language parser needs to predict the correct parse tree for a given sentence, when one needs to optimize a multivariate performance measure like the F1-score, or when predicting the alignment between two proteins. This talk discusses a support vector approach and algorithm for predicting such complex objects. It generalizes conventional classification SVMs to a large range of structured outputs and multivariate loss functions. While the resulting training problems have exponential size, there is a simple algorithm that allows training in polynomial time. The algorithm is implemented in the SVM-Struct software and empirical results will be given for several examples. Speaker Bio: Thorsten Joachims is an Assistant Professor in the Department of Computer Science at Cornell University. In 2001, he finished his dissertation with the title "The Maximum-Margin Approach to Learning Text Classifiers: Methods, Theory, and Algorithms", advised by Prof. Katharina Morik at the University of Dortmund. From there he also received his Diplom in Computer Science in 1997 with a thesis on WebWatcher, a browsing assistant for the Web. From 1994 to 1996 he was a visiting scientist at Carnegie Mellon University with Prof. Tom Mitchell. 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 authored the SVM-Light algorithm and software for support vector learning.