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.
Previous work of the group in this area has dealt with syntactic pattern recognition (mainly grammatical inference), structural pattern recognition (error-tolerant graph matching and probabilistic learning of graph models) and applications in computer vision (object recognition and face recognition). The problem of surface reconstruction from parallel cross sections, typical of 3D medical images, has also been studied, giving rise to a Ph.D. thesis dissertation on this subject. Current interests of SOCO in Pattern Recognition and Computer Vision include dynamic object recognition and tracking from image sequences and bio-inspired hierarchical neural architectures for visual processing.
SOCO works on data and knowledge visualization as a problem that involves artificial pattern recognition as a complement to the natural pattern recognition involved in human visual system. As such, this is a key element of exploratory data mining. Our work focuses on latent variable models, probabilisitic self-organizing systems and Gaussian Processes.
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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