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© 2010

Advances in Machine Learning and Data Analysis

  • Mahyar A. Amouzegar
Book

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 48)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Seyed Eghbal Ghobadi, Omar Edmond Loepprich, Oliver Lottner, Klaus Hartmann, Wolfgang Weihs, Otmar Loffeld
    Pages 1-13
  3. Q. Meng, M. H. Lee, C. J. Hinde
    Pages 15-26
  4. Dejun Xie, David Edwards, Giberto Schleiniger
    Pages 79-94
  5. V. Díaz Casás, P. Porca Belío, F. López Peña, R. J. Duro
    Pages 139-149
  6. Quoc Kien Vuong, Se-Hwan Yun, Suki Kim
    Pages 165-178
  7. Mohammad Golkhah, Mohammad Tavakoli Bina
    Pages 191-202
  8. Jan Broer, Tim Wendisch, Nina Willms
    Pages 203-215
  9. Nathan Percival, Jennifer Percival, Clemens Martin
    Pages 217-230
  10. Scott A. Jeffrey, Brian Cozzarin
    Pages 231-239

About this book

Introduction

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.

Keywords

Computational Modelling Database Management Fuzzy Systems Intelligent Decision Making Optimization Algorithms data analysis machine learning

Editors and affiliations

  • Mahyar A. Amouzegar
    • 1
  1. 1.Dept. Chemical EngineeringCalifornia State UniversityLong BeachUSA

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Reviews

From the reviews: “This is a collection of papers from a large international conference on advances in machine learning and data analysis … . Readers who work with digital systems … would benefit most from this book. … Each chapter has … a bibliography that helps readers find further references, when needed. … the topics covered in this book should be of great interest to researchers and practitioners who want to apply machine learning technology and data analysis tools to problems in general electrical engineering areas … .” (Xiannong Meng, ACM Computing Reviews, March, 2010)