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Minimum Risk Feature Transformations

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Title: Minimum Risk Feature Transformations
Author(s): Agarwal, Shivani; Roth, Dan
Subject(s): algorithms artificial intelligence
Abstract: We develop an approach for automatically learning the optimal feature transformation for a given classification problem. The approach is based on extending the principle of risk minimization (RM), commonly used for learning classifiers, to learning feature transformations that admit classifiers with minimum risk. This allows feature extraction and classification to proceed in a unified framework, both guided by the RM principle. The framework is applied to derive new algorithms for learning feature transformations. Our experiments demonstrate the ability of the resulting algorithms to learn good features for a variety of classification problems.
Issue Date: 2003-12
Genre: Technical Report
Type: Text
URI: http://hdl.handle.net/2142/11089
Other Identifier(s): UIUCDCS-R-2003-2627
Rights Information: You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS: 2009-04-20
 

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