A Review on Hypothesis Representation in Machine Learning

Authors(1) :-Helga V Lobo

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.

Authors and Affiliations

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

Machine Learning, Hypothesis Representation

  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.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 573-575
Manuscript Number : CSEIT1726178
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Helga V Lobo, "A Review on Hypothesis Representation in Machine Learning", International 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.
Journal URL : http://ijsrcseit.com/CSEIT1726178

Article Preview