The Analysis of Patterns, Bertinoro 2007
Automatic pattern analysis of data is a pillar of modern science, technology and business, with deep roots in statistics, machine learning, pattern recognition, theoretical computer science, and many other fields. A unified conceptual understanding of this strategic field is of utmost importance for researchers as well as for users of this technology. Following the successful workshop held in Erice in 2005 - The Analysis of Patterns, Erice 2005 this new edition will combine an emphasis on the common principles of this discipline and a focus on its impact on modern science and technology. Automatically finding trends, anomalies, similarities and any other relation of interest in a dataset is a crucial task for theory and applications, where statistical and algorithmic ideas are intertwined, as well as ideas and methods from information theory, optimization, data mining and machine learning. Very often, different communities focus on different aspects or approaches, so that a general view of the problem is difficult to achieve. The goal of this course is precisely this: to bring together and compare different approaches to the problem of detecting and analyzing any type of patterns in any type of data. This course will deal with general themes arising from the analysis of patterns in different disciplines, theor impact on science and technology. The intended audience are students and researchers in statistcs, computer science, data mining, neural networks and data intensive sciences, interested in pattern analysis. The focus will be on unifying principles that underlie classic disciplines such as sequence pattern matching, pattern recognition by means of machine learning systems, etc.
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