Autor:	Marco Botta, Roberto Piola
Rok vydání:	2000
Předmět:	Knowledge base Artificial Intelligence business.industry Refining Artificial intelligence business Gradient descent Algorithm Software First-order logic Mathematics
Zdroj:	Machine Learning. 38:109-131
ISSN:	0885-6125
DOI:	10.1023/a:1007686007399
Popis:	This paper proposes a method for refining numerical constants occurring in rules of a knowledge base expressed in a first order logic language. The method consists in tuning numerical parameters by performing error gradient descent. The knowledge base to be refined can be manually handcrafted or automatically acquired by a symbolic relational learner, able to deal with numerical features. The results of an experimental analysis performed on four case studies show that the refinement step can be effective in improving classification performances.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fb8b222e46440d727dc3fd00e113842c https://doi.org/10.1023/a:1007686007399 Zobrazit plný text záznamu Full text from SpringerLink