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Boosting first-order learning. (English) Zbl 1184.68423

Arikawa, Setsuo (ed.) et al., Algorithmic learning theory. 7th international workshop, ALT ’96, Sydney, Australia, October 23–25, 1996. Proceedings. Berlin: Springer (ISBN 3-540-61863-5/pbk). Lect. Notes Comput. Sci. 1160, 143-155 (1996).
Summary: Several empirical studies have confirmed that boosting classifier-learning systems can lead to substantial improvements in predictive accuracy. This paper reports early experimental results from applying boosting to ffoil, a first-order system that constructs definitions of functional relations. Although the evidence is less convincing than that for propositional-level learning systems, it suggests that boosting will also prove beneficial for first-order induction.
For the entire collection see [Zbl 0856.68009].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68Q32 Computational learning theory

Software:

C4.5
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