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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Deep, R. Czech, D.R. Dizek, S.G. Kennedy, D.K. |
| Copyright Year | 1988 |
| Description | Author affiliation: Anal. Sci. Corp., Fairborn, OH, USA (Deep, R.; Czech, D.R.; Dizek, S.G.; Kennedy, D.K.) |
| Abstract | A bit-mapped classifier system is described as a simple, effective method of distinguishing among classifiers based on descriptive features. Simple knowledge-representation techniques are used to encode a knowledge base in the domain of product performance agreements (warranties). The user interface is a simple display of features requiring a yes or no answer to indicate applicability. Feature selection is presented that ensures that each classifier can be distinguished from the others. A scoring routine based on features absent, features present, and 'don't care' track the classifiers that best fit the specified description. After starting in a data-driven mode (forward chaining), the system switches to an hypothesis-driven mode and thus economizes on the acquisition of information. A discussion on sequential acquisition information is included to show the value of an expert system that can both forward and backward chain. The system establishes a threshold score for each classifier so that when sufficient points are accumulated, the winning classifier will be declared. An explanation of the selection is given by listing the features that the user designated.< |
| Starting Page | 1222 |
| Ending Page | 1224 |
| File Size | 256454 |
| Page Count | 3 |
| File Format | |
| DOI | 10.1109/NAECON.1988.195161 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1988-05-23 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Expert systems Warranties Knowledge representation Engines Logic testing User interfaces Displays Switches Working environment noise Robustness |
| Content Type | Text |
| Resource Type | Article |
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