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Learning from Data: Concepts, Theory, and Methods

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A learning method is an algorithm (usually implemented in software) that esti- mates an unknown mapping (dependency) between a system’s inputs and outputs from the available data, namely from known (input, output) samples. Once such a dependency has been accurately estimated, it can be used for prediction of future system outputs from the known input values. This book provides a unified descrip- tion of principles and methods for learning dependencies from data.

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

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