Support Vector Machines (SVM) is a rather novel automatic learning paradigm, as well as a hot research issue due to its excellent theoretical foundations and practical results, especially in classification problems. SVMs can be adapted to specific problems through the design of their kernels. SOCO works on kernel design with a focus on methods to deal with incomplete or missing data, as well as on hybridization of Supervised Neural Networks (SNNs) and SVMs, with the design of margin maximization algorithms for SNNs.
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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