Studies of inference rule creation using LAPART
The logical neural architecture LAPART is used in a mode that allows through learning the easy creation and extraction of IF-THEN inference rules from data. This paper first describes ART1 and the complement coded stack input binary representations. Next, we present a more detailed discussion of LAP...
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| container_end_page | ICNN6 vol.3 |
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| container_volume | 3 |
| creator | Caudell, T.P. Healy, M.J. |
| description | The logical neural architecture LAPART is used in a mode that allows through learning the easy creation and extraction of IF-THEN inference rules from data. This paper first describes ART1 and the complement coded stack input binary representations. Next, we present a more detailed discussion of LAPART. Then we show how rules are learned and extracted from the memory templates of the ART1s. We present a pedagogical example of rules extracted from a simple data set. Finally, we note that a fundamental difference between LAPART rule-based systems and regular rule-based systems is the existence of a "rule attractor" that can enhance system generalization in a controlled manner. |
| doi_str_mv | 10.1109/FUZZY.1996.553543 |
| format | Conference Proceeding |
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| language | eng |
| recordid | cdi_ieee_primary_553543 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial intelligence Artificial neural networks Computer architecture Control systems Data mining History Humans Knowledge based systems Machine learning Neural networks |
| title | Studies of inference rule creation using LAPART |
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