CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING
Speed is a significant factor in the implementations of rule-based systems, and many inference engines slow dramatically as the size of the problem increases. Test sets such as Waltz and Manners measure the speed of first order logic inference engines. However, to our knowledge, no test sets for pro...
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2002-01-01
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| Series: | Issues in Information Systems |
| Online Access: | http://iacis.org/iis/2002/Ahmed.pdf |
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doaj-e6dd138b9d1649d88352b3165fb82ced2020-11-25T01:37:55ZengIACISIssues in Information Systems1529-73142002-01-01317348CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAININGShamsuddin AhmedSpeed is a significant factor in the implementations of rule-based systems, and many inference engines slow dramatically as the size of the problem increases. Test sets such as Waltz and Manners measure the speed of first order logic inference engines. However, to our knowledge, no test sets for propositional logic inference engines have heretofore been identified. This paper proposes and tests two test sets that measure the performance of propositional logic inference engines. The first, Chess, measures the speed at which individual rules are tested in a large test set. The second, the Christmas Tree, tests the speed of the chaining process using a binary tree of configurable depths. http://iacis.org/iis/2002/Ahmed.pdf |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Shamsuddin Ahmed |
| spellingShingle |
Shamsuddin Ahmed CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING Issues in Information Systems |
| author_facet |
Shamsuddin Ahmed |
| author_sort |
Shamsuddin Ahmed |
| title |
CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING |
| title_short |
CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING |
| title_full |
CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING |
| title_fullStr |
CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING |
| title_full_unstemmed |
CHARACTER RECOGNITION USING A SELF-ADAPTIVE TRAINING |
| title_sort |
character recognition using a self-adaptive training |
| publisher |
IACIS |
| series |
Issues in Information Systems |
| issn |
1529-7314 |
| publishDate |
2002-01-01 |
| description |
Speed is a significant factor in the implementations of rule-based systems, and many inference engines slow dramatically as the size of the problem increases. Test sets such as Waltz and Manners measure the speed of first order logic inference engines. However, to our knowledge, no test sets for propositional logic inference engines have heretofore been identified. This paper proposes and tests two test sets that measure the performance of propositional logic inference engines. The first, Chess, measures the speed at which individual rules are tested in a large test set. The second, the Christmas Tree, tests the speed of the chaining process using a binary tree of configurable depths. |
| url |
http://iacis.org/iis/2002/Ahmed.pdf |
| work_keys_str_mv |
AT shamsuddinahmed characterrecognitionusingaselfadaptivetraining |
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1725056478196269056 |