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Domain adaptation of conditional probability models via feature subsetting

DSpace at IIT Bombay

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Title Domain adaptation of conditional probability models via feature subsetting
 
Creator SATPAL, S
SARAWAGI, S
 
Description The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabeled data from the target domain to train a model that maximizes likelihood over the training sample while minimizing the distance between the training and target distribution. Our focus is conditional probability models used for predicting a label structure y given input x based on features defined jointly over x and y. We propose practical measures of divergence between the two domains based on which we penalize features with large divergence, while improving the effectiveness of other less deviant correlated features. Empirical evaluation on several real-life information extraction tasks using Conditional Random Fields (CRFs) show that our method of domain adaptation leads to significant reduction in error.
 
Publisher SPRINGER-VERLAG BERLIN
 
Date 2011-10-23T21:53:41Z
2011-12-15T09:11:10Z
2011-10-23T21:53:41Z
2011-12-15T09:11:10Z
2007
 
Type Proceedings Paper
 
Identifier KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2007, PROCEEDINGS,4702,224-235
978-3-540-74975-2
0302-9743
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15247
http://hdl.handle.net/100/1894
 
Source 18th European Conference on Machine Learning (ECML 2007)/11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007),Warsaw, POLAND,SEP 17-21, 2007
 
Language English