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 has carried out work on Feature Selection (FS) both for supervised and unsupervised models: An innovative software for FS in supervised models was developed after an exhaustive review of methods in the Machine Learning literature. It includes a synthetic data generator, an algorithm simulator and an automatic results quality evaluator. SOCO is currently working on a sequential algorithms simulator. Techniques for FS are being developed for Supervised Neural Networks and Support Vector Machines. As for the unsupervised methods, we are currently tackling the less common problem of feature selection in data clustering with mixture models. One of the goals of this type of FS is making compatible the assessment of feature relevance with the improvement of the interpretability of the clustering results through visualization.
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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