Local averaging of heterogeneous regression models

S.B. Kotsiantis

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Local averaging of heterogeneous regression models

机译：异构回归模型的局部平均

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摘要

Many learning problems involve an exploration of relationships between features in heterogeneous datasets, where different learning models can be more suitable for different regions. We propose herein a technique of localized averaging of regression models. This technique identifies local regions which have similar characteristics and then uses the average value of local experts to describe the relationship between the predictive feature values and the target value. We performed a comparison with other famous combining methods on standard benchmark datasets, and the correlation coefficient of the proposed method was higher.

机译：许多学习问题涉及探索异构数据集中要素之间的关系，其中不同的学习模型可能更适合于不同的区域。我们在这里提出一种回归模型的局部平均技术。该技术识别具有相似特征的局部区域，然后使用局部专家的平均值来描述预测特征值与目标值之间的关系。我们在标准基准数据集上与其他著名的合并方法进行了比较，该方法的相关系数更高。

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来源
《International Journal of Hybrid Intelligent Systems》 |2006年第2期|p.99-107|共9页
作者
S.B. Kotsiantis;
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作者单位

Educational Software Development Laboratory, Department of Mathematics, P.A. Box: 1399, University of Patras, Patra 26500, Greece;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
machine learning; data mining; regression; ensembles of regression models;

机译：机器学习;数据挖掘;回归;回归模型集合;

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Local averaging of heterogeneous regression models

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