Extraction of prototype-based threshold rules using neural training procedure
Complex neural and machine learning algorithms usually lack comprehensibility. Combination of sequential covering with prototypes based on threshold neurons leads to a prototype-threshold based rule system. This kind of knowledge representation can be quite efficient, providing solutions to many cla...
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Main Authors: | , , |
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Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2013
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/99223 http://hdl.handle.net/10220/17209 |
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Institution: | Nanyang Technological University |
Language: | English |
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