Ensemble-Based Local Learning for High-Dimensional Data Regression

被引：0

作者：

Raytchev, B. ^{[1
]}

Katamoto, Y. ^{[1
]}

Koujiba, M. ^{[1
]}

Tamaki, T. ^{[1
]}

Kaneda, K. ^{[1
]}

机构：

[1] Hiroshima Univ, Dept Informat Engn, Hiroshima, Japan

来源：

2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2016年

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 propose a new local learning based regression method which utilizes ensemble-learning as a form of regularization to reduce the variance of local estimators. This makes it possible to use local learning methods even with very high-dimensional datasets. The efficacy of the proposed method is illustrated on two publicly available high-dimensional sets in comparison with several global learning methods, and it is shown that the proposed ensemble-based local learning method significantly outperforms the global ones.

引用

页码：2640 / 2645

页数：6

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