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
收藏
页码：1233 / 1236
页数：4
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