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.KeywordsMarine SpongeSpecific GeneralizationInductive Logic ProgrammingHorn ClauseGeneralization RelationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
