Research Theme: Computational Intelligence and Machine Learning
PhD Program In Computer Science and Systems Engineering

Stefano Rovetta, Francesco Masulli
Department: DIBRIS (University of Genova)

Description: Learning machines are currently applied in a variety of problems and settings, including big-size problems (high cardinality and/or dimensionality), in connection with information processing methods inspired to the computation processes going on in living and natural systems, such as Neural Networks, Evolutionary Computation and Fuzzy Logic based systems. New theoretical advancement in this field can boost the methodologies we apply in our applicative projects, including road traffic forecasting, bioinformatics data analysis, supportive technology for “fragile” people, and hedonic IT systems design. In particular, data clustering is a set of methods that have been studied for a long time, and yet still offer room for improvement on both the theoretical and methodological sides. The development of more powerful and flexible data clustering models offers the potential for boosting the data mining capability of automatic systems. During the last decade the development of relational (spectral and kernel) clustering methods has opened new perspectives. Possible outcomes of this research can be the development of effective relational methods to cope with issues related to large data size, throughput, and information content, for example by introducing well-characterized approximations to deal with high-cardinality data, effective data representations to tackle high-dimensional problems, or online versions of relational methods both for low-storage learning and for learning in the presence of concept drift.

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Reference Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern recognition, 41(1), 176-190.