Statistical Learning and Data Sciences

Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings

  • Alexander Gammerman
  • Vladimir Vovk
  • Harris Papadopoulos
Conference proceedings SLDS 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9047)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9047)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Invited Papers

    1. Front Matter
      Pages 1-1
    2. Vladimir Vapnik, Rauf Izmailov
      Pages 33-71
  3. Statistical Learning and Its Applications

    1. Front Matter
      Pages 73-73
    2. Savvas Karatsiolis, Christos N. Schizas
      Pages 75-85
    3. Andreas Henelius, Kai Puolamäki, Isak Karlsson, Jing Zhao, Lars Asker, Henrik Boström et al.
      Pages 96-105
    4. Mikhail Belyaev, Evgeny Burnaev, Yermek Kapushev
      Pages 106-115
    5. Evgeny Burnaev, Maxim Panov
      Pages 116-125
    6. Isak Karlsson, Panagotis Papapetrou, Henrik Boström
      Pages 126-136
    7. Carlo Drago, Carlo Natale Lauro, Germana Scepi
      Pages 147-155
    8. Dmitry Kangin, Plamen Angelov
      Pages 156-168
    9. Clauber Gomes Bezerra, Bruno Sielly Jales Costa, Luiz Affonso Guedes, Plamen Parvanov Angelov
      Pages 169-178
    10. Erik Acorn, Nikos Dipsis, Tamar Pincus, Kostas Stathis
      Pages 179-192
    11. Konstantin Vorontsov, Anna Potapenko, Alexander Plavin
      Pages 193-202
    12. Rapheal Olaniyan, Daniel Stamate, Doina Logofatu
      Pages 203-213
    13. Mohsina Mahmuda Ferdous, Veronica Vinciotti, Xiaohui Liu, Paul Wilson
      Pages 214-222
    14. Miguel Couceiro, Tamás Waldhauser
      Pages 234-238
  4. Conformal Prediction and Its Applications

    1. Front Matter
      Pages 239-239
    2. Huazhen Wang, Xin Liu, Ilia Nouretdinov, Zhiyuan Luo
      Pages 241-250
    3. Lars Carlsson, Ernst Ahlberg, Henrik Boström, Ulf Johansson, Henrik Linusson
      Pages 251-259
    4. Harris Papadopoulos
      Pages 260-270
    5. Ulf Johansson, Ernst Ahlberg, Henrik Boström, Lars Carlsson, Henrik Linusson, Cecilia Sönströd
      Pages 271-280
    6. James Smith, Ilia Nouretdinov, Rachel Craddock, Charles Offer, Alexander Gammerman
      Pages 281-290
    7. Ritvik Jaiswal, Vineeth N. Balasubramanian
      Pages 291-300
    8. Christophe Denis, Mohamed Hebiri
      Pages 301-312
    9. Giovanni Cherubin, Ilia Nouretdinov, Alexander Gammerman, Roberto Jordaney, Zhi Wang, Davide Papini et al.
      Pages 313-322
    10. Ernst Ahlberg, Ola Spjuth, Catrin Hasselgren, Lars Carlsson
      Pages 323-334
  5. New Frontiers in Data Analysis for Nuclear Fusion

    1. Front Matter
      Pages 335-335
    2. A. Murari, E. Peluso, M. Gelfusa, M. Lungaroni, P. Gaudio
      Pages 347-355
    3. Gonzalo Farias, Sebastián Dormido-Canto, Jesús Vega, Norman Díaz
      Pages 356-365
    4. J. Vega, S. Dormido-Canto, F. Martínez, I. Pastor, M. C. Rodríguez
      Pages 366-375
    5. Fernando Pavón, Jesús Vega, Sebastián Dormido Canto
      Pages 376-385
  6. Geometric Data Analysis

    1. Front Matter
      Pages 387-387
    2. Brigitte Le Roux, Frédérik Cassor
      Pages 389-396
    3. Alexander Bernstein, Alexander Kuleshov, Yury Yanovich
      Pages 414-423
    4. Bernard Colin, Jules de Tibeiro, François Dubeau
      Pages 432-441
  7. Back Matter
    Pages 443-444

About these proceedings


This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015.
The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.


artificial neural networks big data classification computational complexity conformal predictors data analysis data mining data sciences evolving system genetic programming information security machine learning model selection optimization particle swarm optimization regularization statistical inference statistical learning support vector machines time series forecasting

Editors and affiliations

  • Alexander Gammerman
    • 1
  • Vladimir Vovk
    • 2
  • Harris Papadopoulos
    • 3
  1. 1.University of LondonEghamUnited Kingdom
  2. 2.University of LondonEghamUnited Kingdom
  3. 3.Frederick UniversityNicosiaCyprus

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-17090-9
  • Online ISBN 978-3-319-17091-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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