Speeding Up Optimum-Path Forest Training by Path-cost Propagation

被引：0

作者：

Iwashita, Adriana S. ^{[1
]}

Papa, Joao P. ^{[1
]}

Falcao, Alexandre X. ^{[2
]}

Lotufo, Roberto A.

de Araujo Oliveira, Victor M. ^{[3
]}

Costa de Albuquerque, Victor H. ^{[4
]}

Tavares, Joao Manuel R. S. ^{[5
]}

机构：

[1] Sao Paulo State Univ, Dept Comp, Bauru, Brazil

[2] Univ Estadual Campinas, Inst Comp, Campinas, SP, Brazil

[3] Univ Estadual Campinas, Sch Elect & Comp Engn, Campinas, SP, Brazil

[4] Univ Fortaleza, Ctr Technol Sci, Fortaleza, Ceara, Brazil

[5] Univ Porto, Fac Engn, P-4100 Porto, Portugal

来源：

2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) | 2012年

基金：

巴西圣保罗研究基金会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1233 / 1236

页数：4

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