Emerging Paradigms in Machine Learning: An Introduction

  • Sheela Ramanna
  • Lakhmi C. Jain
  • Robert J. Howlett
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 13)

Abstract

This chapter provides a broad overview of machine learning (ML) paradigms both emerging as well as well-established ones. These paradigms include: Bayesian Learning, Decision Trees, Granular Computing, Fuzzy and Rough Sets, Inductive Logic Programming, Reinforcement Learning, Neural Networks and Support Vector Machines. In addition, challenges in ML such as imbalanced data, perceptual computing, and pattern recognition of data which is episodic as well as temporal are also highlighted.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sheela Ramanna
    • 1
  • Lakhmi C. Jain
    • 2
  • Robert J. Howlett
    • 3
  1. 1.Dept of Applied Computer ScienceUniversity of WinnipegWinnipegCanada
  2. 2.School of Electrical and Information EngineeringUniversity of South AustraliaAdelaideAustralia
  3. 3.Bournemouth University KES InternationalShoreham-by-seaUnited Kingdom

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