Similarity-Based Pattern Recognition

Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings

  • Aasa Feragen
  • Marcello Pelillo
  • Marco Loog
Conference proceedings SIMBAD 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9370)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 9370)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Veronika Cheplygina, David M. J. Tax
    Pages 15-27
  3. Tewodros M. Dagnew, Umberto Castellani
    Pages 28-37
  4. Manuel Curado, Francisco Escolano, Edwin R. Hancock, Farshad Nourbakhsh, Marcello Pelillo
    Pages 38-53
  5. David G. Márquez, Ana L. N. Fred, Abraham Otero, Constantino A. García, Paulo Félix
    Pages 54-69
  6. Mairelys Hernández-Durán, Veronika Cheplygina, Yenisel Plasencia-Calaña
    Pages 70-83
  7. Elad Hoffer, Nir Ailon
    Pages 84-92
  8. Jian Hou, Chunshi Sha, Hongxia Cui, Lei Chi
    Pages 93-102
  9. André E. Lazzaretti, David M. J. Tax
    Pages 103-116
  10. Pietro Lovato, Manuele Bicego, Vittorio Murino, Alessandro Perina
    Pages 117-132
  11. Yusuf Osmanlıoğlu, Sven Dickinson, Ali Shokoufandeh
    Pages 133-145
  12. Michele Schiavinato, Andrea Gasparetto, Andrea Torsello
    Pages 146-159
  13. Frank-Michael Schleif, Andrej Gisbrecht, Peter Tino
    Pages 160-170
  14. Katerina Zamani, Georgios Paliouras, Dimitrios Vogiatzis
    Pages 171-185
  15. Eyasu Zemene, Samuel Rota Bulò, Marcello Pelillo
    Pages 186-198
  16. Back Matter
    Pages 209-229

About these proceedings


This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015. The 15 full and 8 short papers presented were carefully reviewed and selected from 30 submissions.The workshop focus on problems, techniques, applications, and perspectives: from supervised
to unsupervised learning, from generative to discriminative models, and from
theoretical issues to empirical validations.


Pattern recognition machine learning theory machine learning approaches learning paradigms knowledge representation and reasoning graph representation kernel methods supervised learning unsupervised learning dissimilarity-based learning similarity-based learning non-Euclidean geometry

Editors and affiliations

  • Aasa Feragen
    • 1
  • Marcello Pelillo
    • 2
  • Marco Loog
    • 3
  1. 1.University of CopenhagenCopenhagenDenmark
  2. 2.DAISUniversità Ca' Foscari VeneziaVenezia MestreItaly
  3. 3.Delft University of TechnologyDelftThe Netherlands

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-24260-6
  • Online ISBN 978-3-319-24261-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Chemical Manufacturing
Finance, Business & Banking
IT & Software
Consumer Packaged Goods