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» Combining Logic and Machine Learning for Answering Questions
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ICML
2005
IEEE
14 years 8 months ago
Supervised versus multiple instance learning: an empirical comparison
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
Soumya Ray, Mark Craven
CIKM
2008
Springer
13 years 9 months ago
Kernel methods, syntax and semantics for relational text categorization
Previous work on Natural Language Processing for Information Retrieval has shown the inadequateness of semantic and syntactic structures for both document retrieval and categoriza...
Alessandro Moschitti
CIKM
2005
Springer
14 years 1 months ago
Information retrieval and machine learning for probabilistic schema matching
Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distribute...
Henrik Nottelmann, Umberto Straccia
CADE
2007
Springer
14 years 8 months ago
MaLARea: a Metasystem for Automated Reasoning in Large Theories
MaLARea (a Machine Learner for Automated Reasoning) is a simple metasystem iteratively combining deductive Automated Reasoning tools (now the E and the SPASS ATP systems) with a m...
Josef Urban
AAAI
2008
13 years 10 months ago
Integrating Multiple Learning Components through Markov Logic
This paper addresses the question of how statistical learning algorithms can be integrated into a larger AI system both from a practical engineering perspective and from the persp...
Thomas G. Dietterich, Xinlong Bao