Statistical Learning and Data Sciences

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

  • Alexander Gammerman
  • Vladimir Vovk
  • Harris Papadopoulos

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

About these proceedings

Introduction

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.

Keywords

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 https://doi.org/10.1007/978-3-319-17091-6
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-17090-9
  • Online ISBN 978-3-319-17091-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book
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