A Review on Hypothesis Representation in Machine Learning

Authors

  • Helga V Lobo  Department of Computer Science Engineering, Centre for P.G Studies, VTU, Belagavi, Karnataka, India

Keywords:

Machine Learning, Hypothesis Representation

Abstract

Search through a large hypothesis space is the basis for Machine Learning. The process of learning is said to be complete, when the search arrives at a hypothesis, whichprovides best approximation to the target function. Such hypothesis not only fits the available training examples, but also extends beyond it. This review presents different approaches available for representing a hypothesis and the search of hypothesis space to arrive at the best hypothesis.

References

  1. Tom M Mitchell, "Machine Learning",ISBN-13:978-0070428072.
  2. Mitchell T M(1979).Version Spaces: An approach to concept learning, (Ph.DDissertation).Electrical Engineering Dept., Standford, USA
  3. Fayyad, U.M.(1991). On the induction of decision trees for   multiple   concept learning, (Ph.D Dissertation).EECSDept, University of Michigan.
  4. DeJong,K.A.(1975).An analysis of behavior of a class of genetic adaptive systems((Ph.D  Dissertation).  University of Michigan.
  5. Genetic algorithms in search, optimization , and machine learning. MA: Addison-Wesley.
  6. Waibel,A.,Hanazawa,T.,Hinton.,Shikano,K.,&Lang,K.(1989).Phoneme recognition using time delay neural networks.  IEEE Transactions on Acoustics,Speech and Signal Processing,328-339
  7. Sebag, M(1994).Using Constraints to build version spaces .Proceedings of the 1994 European Conference on Machine learning. Springer-Verlag.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-12-31

Issue

Volume 2, Issue 6, November-December-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Helga V Lobo, " A Review on Hypothesis Representation in Machine Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.573-575, November-December-2017.