Software engineering metrics and models
Software engineering metrics and models
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Neural Networks
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
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Cost estimation is a critical issue for software organizations. Good estimates can help us make more informed decisions (controlling and planning software risks), if they are reliable (correct) and valid (stable). In this study, we apply a variable reduction technique (based on auto-associative feed--forward neural networks - called Curvilinear component analysis) to log-linear regression functions calibrated with ordinary least squares. Based on a COCOMO 81 data set, we show that Curvilinear component analysis can improve the estimation model accuracy by turning the initial input variables into an equivalent and more compact representation. We show that, the models obtained by applying Curvilinear component analysis are more parsimonious, correct, and reliable.