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

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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" 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.