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Previously, we investigated the prediction of total effort and errors for embedded software development projects using an artificial neural network (ANN). In addition, we proposed a method for reducing this margin of error. However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. In this paper,...
In this paper, we create effort prediction models using self-organizing maps (SOMs) for embedded software development projects. SOMs are a type of artificial neural networks that rely on unsupervised learning. They produce a low-dimensional, discretized representation of the input space of training samples, these representations are called maps. SOMs are useful for visualizing low-dimensional views...
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