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Author Muggleton, Stephen ♦ Bratko, Ivan
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract Techniques of machine learning have been successfully applied to various problems [1, 12]. Most of these applications rely on attribute-based learning, exemplified by the induction of decision trees as in the program C4.5 [20]. Broadly speaking, attribute-based learning also includes such approaches to learning as neural networks and nearest neighbor techniques. The advantages of attribute-based learning are: relative simplicity, efficiency, and existence of effective techniques for handling noisy data. However, attribute-based learning is limited to non-relational descriptions of objects in the sense that the learned descriptions do not specify relations among the objects' parts. Attribute-based learning thus has two strong limitations:the background knowledge can be expressed in rather limited form, andthe lack of relations makes the concept description language inappropriate for some domains.
Description Affiliation: Oxford Univ., Oxford, UK (Muggleton, Stephen) || Ljubljana Univ., Ljubljana, Slovenia (Bratko, Ivan)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 38
Issue Number 11
Page Count 6
Starting Page 65
Ending Page 70

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Source: ACM Digital Library