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. © 2012 Springer-Verlag.
CITATION STYLE
Blachnik, M., Kordos, M., & Duch, W. (2012). Extraction of prototype-based threshold rules using neural training procedure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7553 LNCS, pp. 255–262). https://doi.org/10.1007/978-3-642-33266-1_32
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