This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 112
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306768
Title: Multicategory Composite Least Squares Classifiers
Author(s): Seo Young Park*+ and Yufeng Liu
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Address: 1600 Baity Hill Drive #322, Chapel Hill, NC, 27514,
Keywords: multicategory classification ; Fisher consistency ; support vector machine ; regression

Classification is a very useful statistical tool for information extraction. In particular, multicategory classification is commonly seen in various applications. In view of the increased complexity and volume of modern statistical problems, it is desirable to have multicategory classifiers that are able to handle problems with high dimensions and with a large number of classes. In this talk, we propose a novel efficient multicategory composite least squares classifier (CLS classifier), which utilizes a new composite squared loss function. The proposed CLS classifier has several important merits: efficient computation for problems with large number of classes, asymptotic consistency, ability to handle high dimensional data, and simple conditional class probability estimation. Our simulated and real examples demonstrate competitive performance of the proposed approach.

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