Speeding up optimum-path forest training by path-cost propagation
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Abstract
In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.
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English
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Proceedings - International Conference on Pattern Recognition, p. 1233-1236.
