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, and the lack of relations makes the concept description language inappropriate for some domains.
Keywords
Inductive logic programmingComputer scienceSimplicityStatistical relational learningArtificial intelligenceInductive transferMachine learningInstance-based learningArtificial neural networkInductive biasDecision treeMulti-task learningActive learning (machine learning)Relational databaseRobot learningData miningTask (project management)
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Publication Info
- Year
- 1995
- Type
- article
- Volume
- 38
- Issue
- 11
- Pages
- 65-70
- Citations
- 162
- Access
- Closed
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Cite This
Ivan Bratko,
Stephen Muggleton
(1995).
Applications of inductive logic programming.
Communications of the ACM
, 38
(11)
, 65-70.
https://doi.org/10.1145/219717.219771
Identifiers
- DOI
- 10.1145/219717.219771