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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.
Multiclass, classification, one-vs-one, one-vs-all, neural networks, support vector machines.

N. Garc?a-Pedrajas and D. Ortiz-Boyer, "Improving Multiclass Pattern Recognition by the Combination of Two Strategies," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1001-1006, 2006.
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