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The ultimate goal of this book is to bring the fundamental issues of information granularity, inference tools and problem solving procedures into a coherent, unified, and fully operational framework. The objective is to offer the reader a comprehensive, self-contained, and uniform exposure to the subject.The strategy is to isolate some fundamental bricks of Computational Intelligence in terms of key problems and methods, and discuss their implementation and underlying rationale within a well structured and rigorous conceptual framework as well as carefully related to various application facets. The main assumption is that a deep understanding of the key problems will allow the reader to compose into a meaningful mosaic the puzzle pieces represented by the immense varieties of approaches present in the literature and in the computational practice. All in all, the main approach advocated in the monograph consists of a sequence of steps offering solid conceptual fundamentals, presenting a carefully selected collection of design methodologies, discussing a wealth of development guidelines, and exemplifying them with a pertinent, accurately selected illustrative material.
Bruno Apolloni is Full Professor in Computer Science in the University of Milano, Italy. His main research interests are at the frontier area between probability and mathematical statistics and computer science, with special interest to pattern recognition and multivariate data analysis, probabilistic analysis of algorithms, subsymbolic and symbolic learning processes, and fuzzy systems.
Witold Pedrycz is Professor and Canada Research Chair (CRC) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences. His dominant research is in Computational Intelligence, fuzzy modeling, knowledge discovery and data mining, pattern recognition, and knowledge-based neural networks.
Dario Malchiodi is Assistant Professor in the Computer Science Department, University of Milano, Italy. His research activities concern the treatment of uncertain information and related aspects of mathematical statistics and artificial intelligence, including applications to machine learning and relevance learning.
Simone Bassis is an assistant professor in the Department of Computer Science, University of Milano, Italy. His main research activities concern the inference of spatial and temporal processes, including linear and nonlinear statistical regression, fractal processes identification, and evolutionary dynamics.
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