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An Algebraic Approach To Inductive Learning (2000)  (Make Corrections)  (1 citation)
Zdravko Markov
International Journal on Artificial Intelligence Tools



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Abstract: Introduction Inductive learning addresses mainly classification tasks where a series of training examples (instances) are supplied to the learning system and the latter builds an intensional or extensional representation of the examples (hypothesis). The approaches to inductive learning are based mainly on generalization/specialization or similarity-based techniques. Two types of systems are considered here -- concept learning and conceptual clustering. They both generate inductive hypotheses... (Update)

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.... a, b and lgg (a; b) 5 Example To illustrate the semi distance between Horn clauses we use the inductive algorithm described in [3, 2]. The algorithm starts with a given set of examples (ground atoms) GA and builds a hierarchy of Horn clauses covering this examples (i.e. a...

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BibTeX entry:   (Update)

Z. Markov. An algebraic approach to inductive learning. In Proceedings of 13th International FLAIRS Conference, pages 197--201, Orlando, Florida, May 22-25, 2000. AAAI Press. http://citeseer.ist.psu.edu/article/markov00algebraic.html   More

@article{ markov01algebraic,
    author = "Zdravko Markov",
    title = "An Algebraic Approach to Inductive Learning",
    journal = "International Journal on Artificial Intelligence Tools",
    volume = "10",
    number = "1-2",
    pages = "257-272",
    year = "2001",
    url = "citeseer.ist.psu.edu/article/markov00algebraic.html" }
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5   Metrics on partially ordered sets -- a survey (context) - Monjardet - 1981
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2   Lecture Notes in Artificial Intelligence (context) - Hutchinson, terms et al. - 1997
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