Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers | Zendy

Pawan Lingras | Zendy; Cory J. Butz | Zendy

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Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers

Author(s) -

Pawan Lingras,

Cory J. Butz

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28660-8

DOI - 10.1007/11548706_18

Subject(s) - support vector machine , computer science , binary number , rough set , set (abstract data type) , training set , artificial intelligence , data mining , machine learning , pattern recognition (psychology) , mathematics , arithmetic , programming language

The methods for extending binary support vectors machines (SVMs) can be broadly divided into two categories, namely, 1-v-r (one versus rest) and 1-v-1 (one versus one). The 1-v-r approach tends to have higher training time, while 1-v-1 approaches tend to create a large number of binary classifiers that need to be analyzed and stored during the operational phase. This paper describes how rough set theory may help in reducing the storage requirements of the 1-v-1 approach in the operational phase.

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