By Ivan Bratko, Stephen Muggleton
Communications of the ACM,
November 1995,
Vol. 38 No. 11, Pages 65-70
10.1145/219717.219771
Comments
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.
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