Name
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-Naive 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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.ins.2005.07.019 | Inf. Sci. |
Keywords | Field | DocType |
linguistic classifier,linguistic model,query evaluation,mass assignment,certain set,benchmark problem,semi-naive bayes classifier,knowledge representation framework,classification probability,joint mass assignment,bayes classifier,knowledge representation | Data set,Knowledge representation and reasoning,Artificial intelligence,Machine learning,Mathematics,JOINT MASS,Bayes classifier | Journal |
Volume | Issue | ISSN |
176 | 4 | 0020-0255 |
Citations | PageRank | References |
9 | 0.71 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. J. Randon | 1 | 13 | 1.22 |
| Jonathan Lawry | 2 | 172 | 19.06 |