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Information Theory and Statistical Learning

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This book presents theoretical and practical results of information theoretic methods used in the context of statistical learning. Its major goal is to advocate and promote the importance and usefulness of information theoretic concepts for understanding and developing the sophisticated machine learning methods necessary not only to cope with the challenges of modern data analysis but also to gain further insights into their theoretical foundations. Here Statistical Learning is loosely defined as a synonym, for, e.g., Applied Statistics, Artificial Intelligence or Machine Learning. Over the last decades, many approaches and algorithms have been suggested in the fields mentioned above, for which information theoretic concepts constitute core ingredients. For this reason we present a selected collection of some of the finest concepts and applications thereof from the perspective of information theory as the underlying guiding principles.We consider such a perspective as very insightful and expect an even greater appreciation for this perspective over the next years.

The book is intended for interdisciplinary use, ranging from Applied Statistics, Artificial Intelligence, Applied Discrete Mathematics, Computer Science, Information Theory, Machine Learning to Physics. In addition, people working in the hybrid fields of Bioinformatics, Biostatistics, Computational Biology, Computational Linguistics, Medical Bioinformatics, Neuroinformatics orWeb Mining might profit tremendously from the presented results because these data-driven areas are in permanent need of new approaches to cope with the increasing flood of highdimensional, noisy data that possess seemingly never ending challenges for their analysis.
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