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Emerging Paradigms in Machine Learning

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  • © 2013

Overview

Editors:
  1. Sheela Ramanna

    1. Deptartment of Applied Computer Science, University of Winnipeg, Winnipegg, Canada

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  2. Lakhmi C Jain

    1. , School of Electrical and Information, University of South Australia, Adelaide, Australia

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    You can also search for this editor in PubMed   Google Scholar

  3. Robert J. Howlett

    1. KES International, Shoreham-by-sea, United Kingdom

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  • State of the art of emerging paradigms in machine learning including some real world applications
  • Latest research in machine learning and biologically-based techniques for the design and implementation of intelligent systems
  • Written by leading experts in the field

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

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Table of contents (18 chapters)

  1. Front Matter

    Pages 1-20
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  2. Emerging Paradigms in Machine Learning: An Introduction

    • Sheela Ramanna, Lakhmi C. Jain, Robert J. Howlett
    Pages 1-8

  3. Part A: Foundations

    1. Front Matter

      Pages 9-9
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  4. PART A FOUNDATIONS

    1. Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization

      • Abdulaziz Alkhalid, Igor Chikalov, Shahid Hussain, Mikhail Moshkov
      Pages 11-29

    2. Optimised Information Abstraction in Granular Min/Max Clustering

      • Andrzej Bargiela, Witold Pedrycz
      Pages 31-48

    3. Mining Incomplete Data—A Rough Set Approach

      • Jerzy W. Grzymala-Busse, Zdzislaw S. Hippe
      Pages 49-74

    4. Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation

      • Estevam R. Hruschka Jr., Maria do Carmo Nicoletti
      Pages 75-116

    5. Evolving Intelligent Systems: Methods, Algorithms and Applications

      • Andre Lemos, Walmir Caminhas, Fernando Gomide
      Pages 117-159

    6. Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events

      • B. John Oommen, Colin Bellinger
      Pages 161-195

    7. Learning of Defaults by Agents in a Distributed Multi-Agent System Environment

      • Henryk Rybinski, Dominik Ryżko, PrzemysÅ‚aw WiÄ™ch
      Pages 197-213

    8. Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning

      • Hiroshi Sakai, Hitomi Okuma, Michinori Nakata
      Pages 215-247

    9. Introduction to Perception Based Computing

      • Andrzej Skowron, Piotr Wasilewski
      Pages 249-275

    10. Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data

      • Jerzy Stefanowski
      Pages 277-306

    11. A Granular Computing Paradigm for Concept Learning

      • Yiyu Yao, Xiaofei Deng
      Pages 307-326

  5. Part B: Applications

    1. Front Matter

      Pages 327-327
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  6. PART B APPLICATIONS

    1. Identifying Calendar-Based Periodic Patterns

      • Jhimli Adhikari, P. R. Rao
      Pages 329-357

    2. The Mamdani Expert-System with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length

      • Elisabeth Rakus-Andersson
      Pages 359-378

    3. Support Vector Machines in Biomedical and Biometrical Applications

      • Krzysztof A. Cyran, Jolanta Kawulok, Michal Kawulok, Magdalena Stawarz, Marcin Michalak, Monika Pietrowska et al.
      Pages 379-417

    4. Workload Modeling for Multimedia Surveillance Systems

      • Mukesh Saini, Pradeep K. Atrey, Mohan S. Kankanhalli
      Pages 419-440

    5. Rough Set and Artificial Neural Network Approach to Computational Stylistics

      • Urszula StaÅ„czyk
      Pages 441-470
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Keywords

About this book

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.   

Editors and Affiliations

  • Deptartment of Applied Computer Science, University of Winnipeg, Winnipegg, Canada

    Sheela Ramanna

  • , School of Electrical and Information, University of South Australia, Adelaide, Australia

    Lakhmi C Jain

  • KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

Bibliographic Information

  • Book Title: Emerging Paradigms in Machine Learning

  • Editors: Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett

  • Series Title: Smart Innovation, Systems and Technologies

  • DOI: https://doi.org/10.1007/978-3-642-28699-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • Hardcover ISBN: 978-3-642-28698-8Published: 31 July 2012

  • Softcover ISBN: 978-3-642-43574-4Published: 09 August 2014

  • eBook ISBN: 978-3-642-28699-5Published: 31 July 2012

  • Series ISSN: 2190-3018

  • Series E-ISSN: 2190-3026

  • Edition Number: 1

  • Number of Pages: XXII, 498

  • Topics: Computational Intelligence, Artificial Intelligence

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