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Emerging Paradigms in Machine Learning: An Introduction

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Emerging Paradigms in Machine Learning

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

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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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Author information

Authors and Affiliations

  1. Dept of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipeg, MB, R3B 2E9, Canada

    Sheela Ramanna

  2. School of Electrical and Information Engineering, University of South Australia, Adelaide, Mawson Lakes Campus, South Australia SA, 5095, Australia

    Lakhmi C. Jain

  3. Bournemouth University KES International, PO Box 2155, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

Authors
  1. Sheela Ramanna
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  2. Lakhmi C. Jain
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  3. Robert J. Howlett
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Editor information

Editors and Affiliations

  1. Deptartment of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipegg, R3B 2E9, Canada

    Sheela Ramanna

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

  3. KES International, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

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Ramanna, S., Jain, L.C., Howlett, R.J. (2013). Emerging Paradigms in Machine Learning: An Introduction. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_1

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  • DOI: https://doi.org/10.1007/978-3-642-28699-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28698-8

  • Online ISBN: 978-3-642-28699-5

  • eBook Packages: EngineeringEngineering (R0)

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