Over the last few years, SOCO has achieved some advances in the theory and application of Supervised Neural Network models, including studies on activation functions, heterogeneous neurons, Bayesian techniques, Recurrent Networks, sequential construction of Neural Networks and the proposal of a methodology for Feature Selection with Multilayer Perceptrons, based on the Sequential Backward Selection procedure.
The first five describe methodologies with a similar goal: the resolution of complex problems that cannot be efficiently solved by means of traditional computational methods (hard computing). Feature Selection and Extraction deals with problems of data dimensionality reduction that are present in all the previous lines. Finally, Pattern Recognition and Computer Vision copes with a huge application field where the performance of AI systems is usually worse than the human one, and where the soft computing techniques have great potential. Related to it, Data and Knowledge Visualization concerns ways in which Soft Computing complements human vision in problems of exploratory Data Mining.
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