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

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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

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