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Advances in Machine Learning and Data Analysis

  • Book
  • © 2010

Overview

Editors:
  1. Mahyar A. Amouzegar

    1. Dept. Chemical Engineering, California State University, Long Beach, USA

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  • Offers the state of the art of tremendous advances in machine learning and data analysis
  • Serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis
  • Contains sixteen revised and extended research articles written by prominent researchers

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

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-viii
    Download chapter PDF

  2. 2D/3D Image Data Analysis for Object Tracking and Classification

    • Seyed Eghbal Ghobadi, Omar Edmond Loepprich, Oliver Lottner, Klaus Hartmann, Wolfgang Weihs, Otmar Loffeld
    Pages 1-13

  3. Robot Competence Development by Constructive Learning

    • Q. Meng, M. H. Lee, C. J. Hinde
    Pages 15-26

  4. Using Digital Watermarking for Securing Next Generation Media Broadcasts

    • Dominik Birk, Seán Gaines
    Pages 27-41

  5. A Reduced-Dimension Processor Model

    • Azam Beg
    Pages 43-56

  6. Hybrid Machine Learning Model for Continuous Microarray Time Series

    • Sio-Iong Ao
    Pages 57-77

  7. An Asymptotic Method to a Financial Optimization Problem

    • Dejun Xie, David Edwards, Giberto Schleiniger
    Pages 79-94

  8. Analytical Design of Robust Multi-loop PI Controller for Multi-time Delay Processes

    • Truong Nguyen Luan Vu, Moonyong Lee
    Pages 95-108

  9. Automatic and Semi-automatic Methods for the Detection of Quasars in Sky Surveys

    • Sio-Iong Ao
    Pages 109-138

  10. Improving Low-Cost Sail Simulator Results by Artificial Neural Networks Models

    • V. Díaz Casás, P. Porca Belío, F. López Peña, R. J. Duro
    Pages 139-149

  11. Rough Set Approaches to Unsupervised Neural Network Based Pattern Classifier

    • Ashwin Kothari, Avinash Keskar
    Pages 151-163

  12. A New Robust Combined Method for Auto Exposure and Auto White-Balance

    • Quoc Kien Vuong, Se-Hwan Yun, Suki Kim
    Pages 165-178

  13. A Mathematical Analysis Around Capacitive Characteristics of the Current of CSCT: Optimum Utilization of Capacitors of Harmonic Filters

    • Mohammad Golkhah, Mohammad Tavakoli Bina
    Pages 179-190

  14. Harmonic Analysis and Optimum Allocation of Filters in CSCT

    • Mohammad Golkhah, Mohammad Tavakoli Bina
    Pages 191-202

  15. Digital Pen and Paper Technology as a Means of Classroom Administration Relief

    • Jan Broer, Tim Wendisch, Nina Willms
    Pages 203-215

  16. A Conceptual Model for a Network-Based Assessment Security System

    • Nathan Percival, Jennifer Percival, Clemens Martin
    Pages 217-230

  17. Incorrect Weighting of Absolute Performance in Self-Assessment

    • Scott A. Jeffrey, Brian Cozzarin
    Pages 231-239
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Keywords

About this book

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.

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)

Editors and Affiliations

  • Dept. Chemical Engineering, California State University, Long Beach, USA

    Mahyar A. Amouzegar

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