Ensembles of learning machines

被引：0

作者：

Valentini, G ^{[1
]}

Masulli, R

机构：

[1] INFM, I-16146 Genoa, Italy

[2] Univ Genoa, DISI, I-16146 Genoa, Italy

[3] Univ Pisa, Dipartimento Informat, I-56125 Pisa, Italy

来源：

NEURAL NETS | 2002年 / 2486卷

关键词：

ensemble methods; combining multiple learners;

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：

Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

引用

页码：3 / 19

页数：17

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