Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
396444 | Information Sciences | 2006 | 27 Pages |
Abstract
A random set based knowledge representation framework for learning linguistic models is presented. Within this framework a number of algorithms for learning prototypes are proposed, based on grouping certain sets of attributes and evaluating joint mass assignments on labels. These mass assignments can then be combined with a Semi-Naïve Bayes classifier in order to determine classification probabilities. The potential of such linguistic classifiers is then illustrated by their application to a number of toy and benchmark problems. This framework also allows for the evaluation of linguistic queries as will be demonstrated on several well known data sets.
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
N.J. Randon, J. Lawry,