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Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of…mehr

Produktbeschreibung
Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from the author's website
Reflecting emerging concepts and new paradigms in intelligent machines, this is the first book to integrate genetic programming and machine learning techniques for solving a wide range of real-world tasks¿including financial data prediction, day-trading rule development, and bio-marker selection. Written by a leading authority, the book explains how to: use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source code and GUIs are made available for download.
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Autorenporträt
Iba, Hitoshi; Hasegawa, Yoshihiko; Paul, Topon Kumar