Nearest-Neighborhood Linear Regression in an Application with Software Effort Estimation

被引：5

作者：

Leal, Luciana Q. ^{[1
]}

Fagundes, Roberta A. A. ^{[1
]}

de Souza, Renata M. C. R. ^{[1
]}

Moura, Hermano P. ^{[1
]}

Gusmao, Cristine M. G. ^{[2
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil

[2] Univ Estadual Pernambuco, Dept Syst Comp, Recife, PE, Brazil

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9 | 2009年

关键词：

local linear regression; kernel function; LOESS; LOWESS; software effort estimation; VALIDATION;

D O I：

10.1109/ICSMC.2009.5346380

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper discusses nearest-neighborhood linear regression methods in a statistical view of learning and present an application of these models to software project effort estimation. The usefulness of the models is highlighted through experiments with a well-known NASA software project data set. A comparative study with global regression methods such as bagging predictors, support vector regression, radial basis functions neural networks is also introduced.

引用

页码：5030 / +

页数：2

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