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Advances in Machine Learning and Data Analysis (Lecture Notes in Electrical Engineering, 48)

Advances in Machine Learning and Data Analysis (Lecture Notes in Electrical Engineering, 48)

Advances in Machine Learning and Data Analysis (Lecture Notes in Electrical
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Advances in Machine Learning and Data Analysis (Lecture Notes in Electrical Engineering, 48) Paperback - 2012

by Amouzegar, Mahyar

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Springer, 2012-03-14. 2010. paperback. Used: Good. 6.10x0.56x9.25. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Advances in Machine Learning and Data Analysis (Lecture Notes in Electrical Engineering, 48)
  • Author Amouzegar, Mahyar
  • Binding Paperback
  • Edition 2010
  • Condition Used: Good
  • Pages 240
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2012-03-14
  • Bookseller's Inventory # SONG9400730829
  • ISBN 9789400730823 / 9400730829
  • Weight 0.78 lbs (0.35 kg)
  • Dimensions 9.21 x 6.14 x 0.52 in (23.39 x 15.60 x 1.32 cm)
  • Size 6.10x0.56x9.25
  • Category Computers - Data Base Management
  • Dewey Decimal Code 005.73
  • Quantity available 1

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Reader reviews for Advances in Machine Learning and Data Analysis (Lecture Notes in Electrical Engineering, 48)

From the rear cover

A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2008). This volume contains sixteen 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. Advances in Machine Learning and Data Analysis offers the state of the art of tremendous advances in machine learning and data analysis and also serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis.

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