Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 28, Issue 6, Pages 1001-1006Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2006.123
Keywords
multiclass; classification; one-vs-one; one-vs-all; neural networks; support vector machines
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We present a new method of multiclass classification based on the combination of one- vs- all method and a modification of one- vs- one method. This combination of one- vs- all and one- vs- one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided.
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