Machine Learning and Systems Engineering (Lecture Notes in Electrical Engineering, 68, Band 68) [Hardcover] Ao, Sio-Iong; Rieger, Burghard B. and Amouzegar, Mahyar de Ao, Sio-Iong; Rieger, Burghard B.; Amouzegar, Mahyar: Very Good Hardcover (2010)

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N� de ref. del art�culo CEA-2406C-VILNIUS-06-2000

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Detalles bibliogr�ficos

T�tulo: Machine Learning and Systems Engineering (Lecture Notes in Electrical Engineering, 68, Band 68) [Hardcover] Ao, Sio-Iong; Rieger, Burghard B. and Amouzegar, Mahyar
Editor: Springer
Fecha de publicaci�n: 2010
Idioma: Ingl�s
ISBN 10: 9048194180
ISBN 13: 9789048194186
Encuadernaci�n: Hardcover
Estado: Very Good

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Sinopsis

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

De la contraportada

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

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