Inductive concept learning using background knowledge (Article, 1994) [WorldCat.org]
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Inductive concept learning using background knowledge
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Inductive concept learning using background knowledge

Author: Nada Lavrač
Edition/Format:   Article : English
Publication:Proceedings of the XI Brazilian Symposium on Artificial Intelligence, SBIA'94, Fortaleza, October 17-20, [1994] Str. 1-16
Summary:
One of the well-known findings of artificial intelligence (and machine learning) is that the use of domain knowledge is essential for achieving intelligent behaviour. Propositional inductive learning algorithms have a limited capatibility of taking into account the available background knowledge, as well as a limited expressiveness of the knowledge representation formalism used for describing training examples,  Read more...
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Document Type: Article
All Authors / Contributors: Nada Lavrač
OCLC Number: 438799620
Description: Str. 1-16.
Responsibility: Nada Lavrač.

Abstract:

One of the well-known findings of artificial intelligence (and machine learning) is that the use of domain knowledge is essential for achieving intelligent behaviour. Propositional inductive learning algorithms have a limited capatibility of taking into account the available background knowledge, as well as a limited expressiveness of the knowledge representation formalism used for describing training examples, background knowledge and concepts. The paper presents a method for using background knowledge effectively in learning both propositional and relational descriptions, as implemented in the system LINUS. Moreover, it gives a brief introduction to the field of inductive logic programming, a research are of inductive learning, that investigates the methods for the construction of logic programs from examples and background knowledge.

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