Abstract
The aim of relational learning is to develop methods for the induction of descriptions in representation formalisms that are more expressive than attribute-value representation. Feature terms have been studied to formalize object-centered representation in declarative languages and can be seen as a subset of first-order logic. We present a representation formalism based on feature terms and we show how induction can be performed in a natural way using a notion of subsumption based on an informational ordering. Moreover feature terms also allow to specify incomplete information in a natural way. An example of such inductive methods, indie, is presented. indie performs bottom-up heuristic search on the subsumption lattice of the feature term space. Results of this method on several domains are explained.
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© 1997 Springer-Verlag Berlin Heidelberg
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Armengol, E., Plaza, E. (1997). Induction of feature terms with INDIE. In: van Someren, M., Widmer, G. (eds) Machine Learning: ECML-97. ECML 1997. Lecture Notes in Computer Science, vol 1224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62858-4_70
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DOI: https://doi.org/10.1007/3-540-62858-4_70
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