Speeding Up Optimum-Path Forest Training by Path-cost Propagation
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Data
2012-01-01
Autores
Iwashita, Adriana S. [UNESP]
Papa, Joao P. [UNESP]
Falcao, Alexandre X.
Lotufo, Roberto A.
Araujo Oliveira, Victor M. de
Costa de Albuquerque, Victor H.
Tavares, Joao Manuel R. S.
IEEE
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Ieee
Resumo
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 hut with faster data training.
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2012 21st International Conference On Pattern Recognition (icpr 2012). New York: Ieee, p. 1233-1236, 2012.