论文信息 - Extraction of Prototype-Based Threshold Rules Using Neural Training Procedure

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 classification problems with a single rule.

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