Ensemble of Minimal Learning Machines for Pattern Classification

被引：11

作者：

Paiva Mesquita, Diego Parente ^{[1
]}

Pordeus Gomes, Joao Paulo ^{[1
]}

Souza Junior, Amauri Holanda ^{[2
]}

机构：

[1] Fed Inst Ceara, Dept Comp Sci, Fortaleza, Ceara, Brazil

[2] Fed Inst Ceara, Dept Comp Sci, Maracanau, Ceara, Brazil

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II | 2015年 / 9095卷

关键词：

D O I：

10.1007/978-3-319-19222-2_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The use of ensemble methods for pattern classification have gained attention in recent years mainly due to its improvements on classification rates. This paper evaluates ensemble learning methods using the Minimal Learning Machines (MLM), a recently proposed supervised learning algorithm. Additionally, we introduce an alternative output estimation procedure to reduce the complexity of the standard MLM. The proposed methods are evaluated on real datasets and compared to several state-of-the-art classification algorithms.

引用

页码：142 / 152

页数：11

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