Machine Learning and Systems Engineering

  • Sio-Iong Ao
  • Burghard Rieger
  • Mahyar A. Amouzegar

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 68)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Enrico Stoll, Alvar Saenz-Otero, Brent Tweddle
    Pages 1-15
  3. Miloš Šeda, Radomil Matoušek, Pavel Ošmera, Čeněk Šandera, Roman Weisser
    Pages 47-57
  4. Anuj Kumar, I. P. Singh, S. K. Sud
    Pages 59-69
  5. Kashif Gill, Abedalrazq Khalil, Yasir Kaheil, Dennis Moon
    Pages 71-82
  6. David J. Ward, Eric Y. Tao
    Pages 97-109
  7. R. Marichal, J. D. Piñeiro, E. González, J. Torres
    Pages 121-130
  8. Radomil Matousek, Josef Bednar
    Pages 131-142
  9. S. Jassar, Z. Liao, L. Zhao
    Pages 143-155
  10. R. Montenegro, J. M. Cascón, J. M. Escobar, E. Rodríguez, G. Montero
    Pages 157-167
  11. Johannes V. Gragger, Anton Haumer, Markus Einhorn
    Pages 169-181
  12. Jerzy Kozak, Zbigniew GulbinowiczGulbinowicz
    Pages 183-195
  13. Pavan K. Vempaty, Ka C. Cheok, Robert N. K. Loh, Micho Radovnikovich
    Pages 213-225
  14. Wei Zhan, Make McDermott, Behbood Zoghi, Muhammad Hasan
    Pages 227-241
  15. Osama A. Marzouk, E. David Huckaby
    Pages 243-256

About this book

Introduction

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.

Keywords

Software development entropy environment fuzzy knowledge learning machine learning modeling multimedia optimization robot simulation system systems engineering

Editors and affiliations

  • Sio-Iong Ao
    • 1
  • Burghard Rieger
    • 2
  • Mahyar A. Amouzegar
    • 3
  1. 1., Unit 1, 1/FInternational Association of EngineersHong KongHong Kong/PR China
  2. 2.Inst.Computerlinguistik, Abt. Linguistische DatenverarbeitungUniversität TrierTrierGermany
  3. 3.Dept. Chemical EngineeringCalifornia State UniversityLong BeachUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-90-481-9419-3
  • Copyright Information Springer Science+Business Media B.V. 2010
  • Publisher Name Springer, Dordrecht
  • eBook Packages Engineering
  • Print ISBN 978-90-481-9418-6
  • Online ISBN 978-90-481-9419-3
  • Series Print ISSN 1876-1100
  • Series Online ISSN 1876-1119
  • About this book
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