Studies in Fuzziness and Soft Computing

Towards Advanced Data Analysis by Combining Soft Computing and Statistics

Editors: Borgelt, C., Gil, M.Á., Sousa, J.M.C., Verleysen, M. (Eds.)

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  • The book aims to describe how soft computing and statical methods can be used together to improve data analysis 
  • Advances research in soft computing and statical methods for data analysis
  • Written by leading experts in the field
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About this book

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.

Reviews

From the reviews:

“This excellent volume will serve as an introduction to an important merging of viewpoints. The papers are generally very good and the text is clear and readable. The mathematical rigor is high and nearly all papers include real-world examples or experimental data. … This book is a valuable resource for those employed in statistical and soft computing, and a useful work for promoting better connections between these two fields.” (Creed Jones, ACM Computing Reviews, December, 2012)


Table of contents (28 chapters)

Table of contents (28 chapters)
  • Arithmetic and Distance-Based Approach to the Statistical Analysis of Imprecisely Valued Data

    Pages 1-18

    Blanco-Fernández, Angela (et al.)

  • Linear Regression Analysis for Interval-valued Data Based on Set Arithmetic: A Review

    Pages 19-31

    Blanco-Fernández, Angela (et al.)

  • Bootstrap Confidence Intervals for the Parameters of a Linear Regression Model with Fuzzy Random Variables

    Pages 33-42

    Ferraro, Maria Brigida (et al.)

  • On the Estimation of the Regression Model M for Interval Data

    Pages 43-52

    García-Bárzana, Marta (et al.)

  • Hybrid Least-Squares Regression Modelling Using Confidence Bounds

    Pages 53-63

    Tütmez, Bülent (et al.)

Buy this book

eBook $129.00
price for USA in USD (gross)
  • ISBN 978-3-642-30278-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $199.99
price for USA in USD
  • ISBN 978-3-642-30277-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $169.99
price for USA in USD
  • ISBN 978-3-642-44374-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Towards Advanced Data Analysis by Combining Soft Computing and Statistics
Editors
  • Christian Borgelt
  • María Ángeles Gil
  • João M.C. Sousa
  • Michel Verleysen
Series Title
Studies in Fuzziness and Soft Computing
Series Volume
285
Copyright
2013
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-30278-7
DOI
10.1007/978-3-642-30278-7
Hardcover ISBN
978-3-642-30277-0
Softcover ISBN
978-3-642-44374-9
Series ISSN
1434-9922
Edition Number
1
Number of Pages
X, 378
Topics