| Multicategory Composite Least Squares Classifiers. | |
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MedLine Citation:
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PMID: 21218128 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Classification is a very useful statistical tool for information extraction. In particular, multicategory classification is commonly seen in various applications. Although binary classification problems are heavily studied, extensions to the multicategory case are much less so. 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. Moreover, it is necessary to have sound theoretical properties for the multicategory classifiers. In the literature, there exist several different versions of simultaneous multicategory Support Vector Machines (SVMs). However, the computation of the SVM can be difficult for large scale problems, especially for problems with large number of classes. Furthermore, the SVM cannot produce class probability estimation directly. In this article, 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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Authors:
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Seo Young Park; Yufeng Liu; Dacheng Liu; Paul Scholl |
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Publication Detail:
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Type: JOURNAL ARTICLE |
Journal Detail:
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Title: Statistical analysis and data mining Volume: 3 ISSN: 1932-1872 ISO Abbreviation: - Publication Date: 2010 Aug |
Date Detail:
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Created Date: 2011-1-10 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101492808 Medline TA: Stat Anal Data Min Country: - |
Other Details:
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Languages: ENG Pagination: 272-286 Citation Subset: - |
Affiliation:
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Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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| Grant Support | |
ID/Acronym/Agency:
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R01 CA149569-01//NCI NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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