Emerging Paradigms in Machine Learning

  • Sheela Ramanna
  • Lakhmi C Jain
  • Robert J. Howlett

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 13)

Table of contents

  1. Front Matter
    Pages 1-20
  2. Sheela Ramanna, Lakhmi C. Jain, Robert J. Howlett
    Pages 1-8
  3. Part A: Foundations

    1. Front Matter
      Pages 9-9
    2. Abdulaziz Alkhalid, Igor Chikalov, Shahid Hussain, Mikhail Moshkov
      Pages 11-29
    3. Andrzej Bargiela, Witold Pedrycz
      Pages 31-48
    4. Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe
      Pages 49-74
    5. Estevam R. Hruschka Jr., Maria do Carmo Nicoletti
      Pages 75-116
    6. Andre Lemos, Walmir Caminhas, Fernando Gomide
      Pages 117-159
    7. Henryk Rybinski, Dominik Ryżko, Przemysław Więch
      Pages 197-213
    8. Andrzej Skowron, Piotr Wasilewski
      Pages 249-275
    9. Yiyu Yao, Xiaofei Deng
      Pages 307-326
  4. Part B: Applications

    1. Front Matter
      Pages 327-327
    2. Jhimli Adhikari, P. R. Rao
      Pages 329-357
    3. Krzysztof A. Cyran, Jolanta Kawulok, Michal Kawulok, Magdalena Stawarz, Marcin Michalak, Monika Pietrowska et al.
      Pages 379-417
    4. Mukesh Saini, Pradeep K. Atrey, Mohan S. Kankanhalli
      Pages 419-440

About this book

Introduction

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.   

Keywords

Emerging paradigms Emerging paradigms Intelligent systems Intelligent systems Machine learning Machine learning Smart systems Smart systems

Editors and affiliations

  • Sheela Ramanna
    • 1
  • Lakhmi C Jain
    • 2
  • Robert J. Howlett
    • 3
  1. 1.Deptartment of Applied Computer ScienceUniversity of WinnipegWinnipeggCanada
  2. 2., School of Electrical and InformationUniversity of South AustraliaAdelaideAustralia
  3. 3.KES InternationalShoreham-by-seaUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-28699-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-28698-8
  • Online ISBN 978-3-642-28699-5
  • Series Print ISSN 2190-3018
  • Series Online ISSN 2190-3026
  • About this book