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A framework for Kernel-based multi-category classification

Abstract:

A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods. The ability of the framework to compare algorithms is illustrated by a brief discussion of Fisher consistency. Its utility in improving understandin...

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Publication status:
Published

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH More from this journal
Volume:
30
Pages:
525-564
Publication date:
2007-01-01
EISSN:
1076-9757
ISSN:
1076-9757
Language:
English
Pubs id:
pubs:172686
UUID:
uuid:e6cf7094-62a1-4a3f-9199-d00f0e761a6e
Local pid:
pubs:172686
Source identifiers:
172686
Deposit date:
2012-12-19

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