The goal of Inductive Learning is to produce general rules from a set of seen
examples, which can then be applied to other unseen examples. ID3 is an inductive
learning algorithm that can be used for the classification task. The input to the
algorithm is a set of tuples of description and class. The ID3 algorithm learns
a decision tree from these input examples, which can then be used for classifying
unseen examples given their descriptions. ID3 faces a problem called the replication
problem.
An algorithm called the Expert-Gate algorithm is presented in this thesis. The
aim of the algorithm is to tackle the replication problem. We discuss the various
issues involved with each step of the algorithm and present results corroborating
our choices. The algorithm was tested on various artificially created problems as
well as on a real life problem. The performance of the algorithm was compared
with that of Fringe.
The algorithm was found to give excellent results on the artificially created
problems. The Expert-Gate algorithm gave satisfactory results on the NETtalk
problem. Overall, we believe the algorithm is a good candidate for testing on other
real life domains. / Graduation date: 1993
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/36248 |
| Date | 23 November 1992 |
| Creators | Joshi, Varad Vidyadhar |
| Contributors | Dietterich, Thomas G. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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