Speeding up optimum-path forest training by path-cost propagation
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Data
2012-12-01
Autores
Iwashita, Adriana S. [UNESP]
Papa, João Paulo [UNESP]
Falcao, Alexandre X.
Lotufo, Roberto A.
De Araujo Oliveira, Victor M.
De Albuquerque, Victor H. Costa
Tavares, Joao Manuel R. S.
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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 but with faster data training. © 2012 ICPR Org Committee.
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Minimum spanning forests, Optimum-path forests, Software engineering, Pattern recognition
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Proceedings - International Conference on Pattern Recognition, p. 1233-1236.