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Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

  • Serkan Kiranyaz
  • Turker Ince
  • Moncef Gabbouj

Part of the Adaptation, Learning, and Optimization book series (ALO, volume 15)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 1-11
  3. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 13-44
  4. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 45-82
  5. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 83-99
  6. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 101-149
  7. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 151-186
  8. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 187-230
  9. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 231-258
  10. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 259-294
  11. Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
    Pages 295-321

About this book

Introduction

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach.

 

After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets.

 

The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.

Keywords

Content-based image retrieval Data clustering Evolutionary computing Evolutionary feature synthesis Evolutionary neural networks Image classification Machine learning Multidimensional particle swarm optimization (PSO) Optimization Personalized ECG classification

Authors and affiliations

  • Serkan Kiranyaz
    • 1
  • Turker Ince
    • 2
  • Moncef Gabbouj
    • 3
  1. 1., Dept. of Signal ProcessingTampere University of TechnologyTampereFinland
  2. 2., Dept. of Elect. & Electronics EngIzmir University of EconomicsBalcovaTurkey
  3. 3., Dept. of Signal ProcessingTampere University of TechnologyTampereFinland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-37846-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-37845-4
  • Online ISBN 978-3-642-37846-1
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • Buy this book on publisher's site
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