Advertisement

Aggregation Functions: A Guide for Practitioners

  • Gleb Beliakov
  • Ana Pradera
  • Tomasa Calvo

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 221)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 1-37
  3. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 39-122
  4. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 123-196
  5. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 197-260
  6. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 261-269
  7. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 271-296
  8. Gleb Beliakov, Ana Pradera, Tomasa Calvo
    Pages 297-304
  9. Back Matter
    Pages 305-361

About this book

Introduction

Aggregation of information is of primary importance in the construction of knowledge based systems in various domains, ranging from medicine, economics, and engineering to decision-making processes, artificial intelligence, robotics, and machine learning. This book gives a broad introduction into the topic of aggregation functions, and provides a concise account of the properties and the main classes of such functions, including classical means, medians, ordered weighted averaging functions, Choquet and Sugeno integrals, triangular norms, conorms and copulas, uninorms, nullnorms, and symmetric sums. It also presents some state-of-the-art techniques, many graphical illustrations and new interpolatory aggregation functions. A particular attention is paid to identification and construction of aggregation functions from application specific requirements and empirical data. This book provides scientists, IT specialists and system architects with a self-contained easy-to-use guide, as well as examples of computer code and a software package. It will facilitate construction of decision support, expert, recommender, control and many other intelligent systems.

Keywords

artificial intelligence computer construction intelligence intelligent systems knowledge base learning machine learning robot robotics

Authors and affiliations

  • Gleb Beliakov
    • 1
  • Ana Pradera
    • 2
  • Tomasa Calvo
    • 3
  1. 1.School of Engineering and ITDeakin UniversityBurwood 3125Australia
  2. 2.Escuela Superior de CienciasExperimentales y TecnologíaMadridSpain
  3. 3.Escuela Técnica Superior de Ingeniería InformáticaUniversidad de AlcaláMadridSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-73721-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-73720-9
  • Online ISBN 978-3-540-73721-6
  • Series Print ISSN 1434-9922
  • Buy this book on publisher's site