(Enter summary)
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)
Context of citations to this paper: More
.... 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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