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Advances in Self-Organizing Maps and Learning Vector Quantization

Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016

  • Erzsébet Merényi
  • Michael J. Mendenhall
  • Patrick O'Driscoll

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 428)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Self-Organizing Map Learning, Visualization, and Quality Assessment

    1. Front Matter
      Pages 1-1
    2. Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Nathalie Villa-Vialaneix
      Pages 3-26
    3. Jérôme Mariette, Nathalie Villa-Vialaneix
      Pages 27-37
    4. Alfred Ultsch, Martin Behnisch, Jörn Lötsch
      Pages 39-48
    5. Denny, William Gozali, Ruli Manurung
      Pages 61-71
    6. Madalina Olteanu, Nathalie Villa-Vialaneix
      Pages 73-82
  3. Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas

    1. Front Matter
      Pages 83-83
    2. Yaser Moazzen, Kadim Taşdemir
      Pages 85-93
    3. Jorge R. Vergara, Pablo A. Estévez, Álvaro Serrano
      Pages 107-117
    4. Rigoberto Fonseca-Delgado, Pilar Gómez-Gil
      Pages 119-128
  4. Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps

    1. Front Matter
      Pages 129-129
    2. Paulo Henrique Muniz Ferreira, Aluízio Fausto Ribeiro Araújo
      Pages 131-141
    3. Mehdi Abadi, Slavisa Jovanovic, Khaled Ben Khalifa, Serge Weber, Mohamed Hédi Bedoui
      Pages 165-175
    4. Humberto I. Fontinele, Davyd B. Melo, Guilherme A. Barreto
      Pages 177-187
  5. Self-Organizing Maps in Neuroscience and Medical Applications

    1. Front Matter
      Pages 189-189
    2. Nahed Alowadi, Yuan Shen, Peter Tiňo
      Pages 193-203
    3. Deborah Mudali, Michael Biehl, Klaus L. Leenders, Jos B. T. M. Roerdink
      Pages 205-215
    4. Benjamin D. Kramer, Dylan P. Losey, Marcia K. O’Malley
      Pages 227-237
    5. Masaaki Ohkita, Heizo Tokutaka, Nobuhiko Kasezawa, Eikou Gonda
      Pages 239-249
    6. Patrick O’Driscoll, Erzsébet Merényi, Christof Karmonik, Robert Grossman
      Pages 251-263
  6. Learning Vector Quantization Theories and Applications I

    1. Front Matter
      Pages 265-265
    2. T. Villmann, M. Kaden, A. Bohnsack, J.-M. Villmann, T. Drogies, S. Saralajew et al.
      Pages 269-279
    3. Matthias Gay, Marika Kaden, Michael Biehl, Alexander Lampe, Thomas Villmann
      Pages 293-303
    4. David Nova, Pablo A. Estévez
      Pages 305-314
  7. Learning Vector Quantization Theories and Applications II

    1. Front Matter
      Pages 315-315
    2. Friedrich Melchert, Udo Seiffert, Michael Biehl
      Pages 317-327
    3. Kerstin Bunte, Marika Kaden, Frank-Michael Schleif
      Pages 341-353
    4. Jonathon Climer, Michael J. Mendenhall
      Pages 355-368
  8. Back Matter
    Pages 369-370

About these proceedings

Introduction

This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.
The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.

Keywords

Computational Intelligence Intelligent Systems LVQ Learning Vector Quantization SOM Self-Organizing Maps WSOM 2016

Editors and affiliations

  • Erzsébet Merényi
    • 1
  • Michael J. Mendenhall
    • 2
  • Patrick O'Driscoll
    • 3
  1. 1.Department of StatisticsRice UniversityHoustonUSA
  2. 2.Department of Electrical and Computer EnAir Force Institute of TechnologyWright-Patterson AFBUSA
  3. 3.Applied PhysicsRice UniversityHoustonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-28518-4
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-28517-7
  • Online ISBN 978-3-319-28518-4
  • Series Print ISSN 2194-5357
  • Series Online ISSN 2194-5365
  • Buy this book on publisher's site
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