Synergies of Soft Computing and Statistics for Intelligent Data Analysis

  • Rudolf Kruse
  • Michael R. Berthold
  • Christian Moewes
  • María Ángeles Gil
  • Przemysław Grzegorzewski
  • Olgierd Hryniewicz

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)

Table of contents

  1. Front Matter
    Pages 1-14
  2. Invited Papers

    1. Front Matter
      Pages 1-1
    2. Christian Borgelt
      Pages 3-10
    3. Hannu Toivonen, Oskar Gross, Jukka M. Toivanen, Alessandro Valitutti
      Pages 17-24
  3. Foundations

    1. Front Matter
      Pages 25-25
    2. Renato Pelessoni, Paolo Vicig
      Pages 37-44
    3. Erik Quaeghebeur
      Pages 45-54
    4. Oliver Strauss, Agnes Rico
      Pages 55-62
    5. Alessandro Ferrero, Marco Prioli, Simona Salicone, Barbara Vantaggi
      Pages 63-72
    6. Tommaso Flaminio, Lluís Godo Lacasa
      Pages 73-81
  4. Statistical Methods

    1. Front Matter
      Pages 103-103
    2. Angela Blanco-Fernández, Ana Colubi, Marta García-Bárzana, Manuel Montenegro
      Pages 105-113
    3. Maria Brigida Ferraro, Paolo Giordani
      Pages 115-123
    4. Ana Belén Ramos-Guajardo, María Asunción Lubiano, Gil González-Rodríguez
      Pages 125-133
    5. Sara de la Rosa de Sáa, María Ángeles Gil, María Teresa López García, María Asunción Lubiano
      Pages 135-143

About these proceedings


In recent years there has been a growing interest to extend classical methods for data analysis.
The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance.
Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled.
About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS).

This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis.
It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics.
Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.


Computational Intelligence Intelligent Data Analysis Soft Computing

Editors and affiliations

  • Rudolf Kruse
    • 1
  • Michael R. Berthold
    • 2
  • Christian Moewes
    • 3
  • María Ángeles Gil
    • 4
  • Przemysław Grzegorzewski
    • 5
  • Olgierd Hryniewicz
    • 6
  1. 1.Faculty of Computer ScienceOtto-von-Guericke University of MagdeburMagdeburgGermany
  2. 2., FB Informatik & InformationswissenschaftUniversity of KonstanzKonstanzGermany
  3. 3.of Magdeburg, Faculty of Computer ScienceOtto-von-Guericke UniversityMagdeburgGermany
  4. 4., Department of Statistics and ORUniversity of OviedoOviedoSpain
  5. 5.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  6. 6.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-33041-4
  • Online ISBN 978-3-642-33042-1
  • Series Print ISSN 2194-5357
  • Series Online ISSN 2194-5365
  • About this book