PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multiclass Learning at One-Class Complexity
Sandor Szedmak and John Shawe-Taylor
(2005) Technical Report. School of Electronics and Computer Science, Southampton, UK.

Abstract

We show in this paper the multiclass classification problem can be implemented in the maximum margin framework with the complexity of one binary Support Vector Machine. We show reducing the complexity does not involve diminishing performance but in some cases this approach can improve the classification accuracy. The multiclass classification is realized in the framework where the output labels are vector valued.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1508
Deposited By:Sandor Szedmak
Deposited On:28 November 2005