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)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Angelov, P., Filev, D.P., Kasabov, N.K.: Evolving Intelligent Systems: Methodology and Applications. Wiley and IEEE Series of Computational Intelligence (2010)Google Scholar
  2. 2.
    Barto, A.G., Sutton, R.S., Brouwer, P.S.: Associative Search Network: A reinforcement learning associative memory. Biological Cybernetics 40, 201–211 (1981)MATHCrossRefGoogle Scholar
  3. 3.
    Bayes, T., Price, M.: An Essay towards solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London 53, 370–418 (1763)Google Scholar
  4. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006) ISBN 0-387-31073-8Google Scholar
  5. 5.
    Bosen, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, Pittsburgh (1992)Google Scholar
  6. 6.
    Concise Oxford Dictionary, 12th edn. Oxford University Press (2011), 978-0-19- 960108-0Google Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20 (1995)Google Scholar
  8. 8.
    Cristianini, N., Campbell, C., Burges, C.: Kernel Methods: Current Research and Future Directions. Machine Learning 46, 5–9 (2002)CrossRefGoogle Scholar
  9. 9.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2), 5:3–5:60 (2008)CrossRefGoogle Scholar
  10. 10.
    Fahle, M., Poggio, T.: Perceptual Learning. MIT Press (2002)Google Scholar
  11. 11.
    Fechner, G.: Elemente der Psychophysik, vol. 2. E.J. Bonset, Amsterdam (1860)Google Scholar
  12. 12.
    Farley, B.G., Clark, W.: Simulation of self-organzing systems by digital computer. I.R.E. Transactions on Information Theory 4, 76–84 (1954)MathSciNetGoogle Scholar
  13. 13.
    Feigenbaum, E.A.: The Simulation of Verbal Learning Behavior. In: Proceedings of the Western Joint Computer Conference, vol. 19, pp. 121–132 (1961)Google Scholar
  14. 14.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer (2008) ISBN 0-387-95284-5Google Scholar
  15. 15.
    Holland, J.: Adaptation in Natural and Artificial Systems. The Universityof Michigan Press, Ann Arbor (1975)Google Scholar
  16. 16.
    Leibniz, G.W.: New Essays in Human Understanding. Cambridge University Press, Cambridge (1705)Google Scholar
  17. 17.
    McCulloch, W.S., Pitts, W.H.: Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Mendel, J.M.: A survey of learning control systems. ISA Transactions 5, 297–303 (1966)Google Scholar
  19. 19.
    Mitchell, T.: Machine Learning. McGraw Hill (1997) ISBN 0-07-042807-7Google Scholar
  20. 20.
    Minsky, M.L.: Theory of neural-analog reinforcement learning systems and its applicationto the brain-model problem. Ph.D Dissertation, Princeton University (1954)Google Scholar
  21. 21.
    Nilsson, N.J.: Learning Machines. McGraw-Hill (1965)Google Scholar
  22. 22.
    Nilsson, N.J.: Introduction to Machine Learning, Notes. Stanford University (2010)Google Scholar
  23. 23.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)MathSciNetMATHCrossRefGoogle Scholar
  24. 24.
    Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177, 3–27 (2007)MathSciNetMATHCrossRefGoogle Scholar
  25. 25.
    Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177, 28–40 (2007)MathSciNetMATHCrossRefGoogle Scholar
  26. 26.
    Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177, 41–73 (2007)MathSciNetMATHCrossRefGoogle Scholar
  27. 27.
    Pedrycz, W., Skowron, A., Kreinovich, V.: Handbook of Granular Computing. Wiley (2008)Google Scholar
  28. 28.
    Peters, J.F., Skowron, A.: Transactions on Rough Sets. Springer (2004)Google Scholar
  29. 29.
    Peters, J.F.: Near sets. Special theory about nearness of objects. Fundamental Informaticae 75(1-4), 407–433 (2007)MATHGoogle Scholar
  30. 30.
    Peters, J.F.: Near sets. General theory about nearness of objects. Applied Mathematical Sciences 1(53), 2609–2629 (2007)MathSciNetMATHGoogle Scholar
  31. 31.
    Peters, J.F.: Approximation and Perception in Ethology Based Rein-forcement Learning. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 670–688. Wiley (2008)Google Scholar
  32. 32.
    Peters, J.F., Henry, C., Ramanna, S.: Reinforcement learning in swarms that learn. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2005), France, pp. 400–406 (2005)Google Scholar
  33. 33.
    Rosenblatt, F.: The perceptron: A perceiving and recognizing automaton. Rep. No. 85-460-1, Project PARA. Cornell Aeronautical Laboratory, Buffalo NY (1957)Google Scholar
  34. 34.
    Sutton, R.S.: Temporal Credit Assignment in Reinforcement Learning. Ph.D Dissertation. University of Massachussetts (1984)Google Scholar
  35. 35.
    Vapnik, V.: The nature of statistical learning theory. Springer (1995) ISBN 0-387-98780-0Google Scholar
  36. 36.
    Whiteson, S., Littman, M.L.: Introduction to the special issue on empirical evaluationsin reinforcement learning. Machine Learning 84, 1–6 (2011), doi:10.1007/s10994-011-5255-6CrossRefGoogle Scholar
  37. 37.
    Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., Srinivasan, A.: ILP turns 20 Biography and future challenges. Machine Learning (2011), doi:10.1007/s10994-011-5259-2Google Scholar
  38. 38.
    Weber, E.H.: De pulsu, resorptione, auditu et tactu. Anatatione-sanatomicae et physiologicae. Koehler, Leipzig (1834)Google Scholar
  39. 39.
    Zadeh, L.A.: Toward a theory of Fuzzy Information Granulation and Its Certainty in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90, 111–127 (1997)MathSciNetMATHCrossRefGoogle Scholar
  40. 40.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338–353 (1965)MathSciNetMATHCrossRefGoogle Scholar

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

Personalised recommendations