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Towards Advanced Data Analysis by Combining Soft Computing and Statistics

  • Christian Borgelt
  • María Ángeles Gil
  • João M.C. Sousa
  • Michel Verleysen

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)

Table of contents

  1. Front Matter
    Pages 1-7
  2. Angela Blanco-Fernández, María Rosa Casals, Ana Colubi, Renato Coppi, Norberto Corral, Sara de la Rosa de Sáa et al.
    Pages 1-18
  3. Angela Blanco-Fernández, Ana Colubi, Gil González-Rodríguez
    Pages 19-31
  4. Maria Brigida Ferraro, Renato Coppi, Gil González-Rodríguez
    Pages 33-42
  5. Marta García-Bárzana, Ana Colubi, Erricos J. Kontoghiorghes
    Pages 43-52
  6. Ana Belén Ramos-Guajardo, Gil González-Rodríguez
    Pages 65-74
  7. Wolfgang Trutschnig, María Asunción Lubiano, Julia Lastra
    Pages 107-118
  8. Piotr Nowak, Maciej Romaniuk
    Pages 137-150
  9. Alexandru Mandes, Cristian Gatu, Peter Winker
    Pages 151-163
  10. Dejan Petelin, Juš Kocijan
    Pages 177-190
  11. Katharina Tschumitschew, Frank Klawonn
    Pages 191-203
  12. Pascal Held, Christian Moewes, Christian Braune, Rudolf Kruse, Bernhard A. Sabel
    Pages 205-222
  13. Mario Rosario Guarracino, Raimundas Jasinevicius, Radvile Krusinskiene, Vytautas Petrauskas
    Pages 223-240
  14. Dubravko Ćulibrk, Matei Mancas, Vladimir Ćrnojevic
    Pages 253-266
  15. Thomas Low, Christian Borgelt, Sebastian Stober, Andreas Nürnberger
    Pages 267-278
  16. Shima Gerani, Mostafa Keikha, Fabio Crestani
    Pages 279-290
  17. Kristian Loewe, Marcus Grueschow, Christian Borgelt
    Pages 305-317
  18. Didier Dubois, Daniel Sánchez
    Pages 319-330
  19. Nikos S. Thomaidis, Vassilios Vassiliadis
    Pages 343-357
  20. Özlem Türkşen, Susana M. Vieira, José F. A. Madeira, Ayşen Apaydin, João M. C. Sousa
    Pages 359-375
  21. Back Matter
    Pages 0--1

About this book

Introduction

Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis.
Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty.
Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their (relative) likelihood. It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance.
Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively.

Keywords

Data Analysis Solutions Distribution Algorithms Estimators Evolutionary Algorithms Model Selection Neural Networks Psychological Complexity Soft Computing Statistical Complexity Statistics

Editors and affiliations

  • Christian Borgelt
    • 1
  • María Ángeles Gil
    • 2
  • João M.C. Sousa
    • 3
  • Michel Verleysen
    • 4
  1. 1.Intelligent Data Analysis & Graphical, Models Research UnitEuropean Centre for Soft ComputingMieresSpain
  2. 2., Departamento de Estadistica e I. O. yUniversidad de OviedoOviedoSpain
  3. 3.Instituto Superior Técnico, Department of Mechanical EngineeringTechnical University LisbonLisboaPortugal
  4. 4.Labo. MicroelectroniqueUniversité Catholique de LouvainLeuvenBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-30278-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-30277-0
  • Online ISBN 978-3-642-30278-7
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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