@ElectronicSource{TrontoSilvAnna::ArNeNe,
abstract = "Machine learning techniques such as neural networks, rule
induction, genetic algorithm and case-based reasoning are finding
application in a wide variety of fields such as computer vision,
econometrics and medicine, where human abilities have proven to be
superior to those of computers. Such techniques hold the promise
of being able to make sense of a variety of inputs of different
types in producing an output. Software effort modeling has always
appeared to be a rather hit-or-miss business where statistical
methods frequently result in low accuracy of prediction. Some
experiments using an artificial neural networks have been
conducted, highlighting some of the problems that arise when
machine learning techniques are applied to software effort
modeling. These experiments show that, compared with conventional
regression analysis, improved accuracy of prediction is
possible.",
address = "S{\~a}o Jos{\'e} dos Campos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
author = "Tronto, Iris Fabiana de Barcelos and Silva, Jose Demisio Simoes da
and Anna, Nilson Sant",
keywords = "Software effort estimation, neural network model, regression
analysis.",
lastupdatedate = "2006-12-21",
publisher = "Instituto and Nacional and de and Pesquisas and Espaciais",
ibi = "sid.inpe.br/ePrint@80/2006/12.20.23.38",
url = "http://urlib.net/ibi/sid.inpe.br/ePrint@80/2006/12.20.23.38",
targetfile = "v1.pdf",
title = "The artificial neural networks model for software effort
estimation",
typeofmedium = "On-line",
urlaccessdate = "27 abr. 2024"
}