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ICMLA
2009
14 years 10 months ago
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
87
Voted
ML
2006
ACM
105views Machine Learning» more  ML 2006»
15 years 14 days ago
Propositionalization-based relational subgroup discovery with RSD
Abstract Relational rule learning algorithms are typically designed to construct classification and prediction rules. However, relational rule learning can be adapted also to subgr...
Filip Zelezný, Nada Lavrac
ICML
1996
IEEE
16 years 1 months ago
Learning Relational Concepts with Decision Trees
In this paper, we describe two di erent learning tasks for relational structures. When learning a classi er for structures, the relational structures in the training sets are clas...
Peter Geibel, Fritz Wysotzki
EMNLP
2011
14 years 8 days ago
Relation Extraction with Relation Topics
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge l...
Chang Wang, James Fan, Aditya Kalyanpur, David Gon...
98
Voted
ICML
2003
IEEE
16 years 1 months ago
Learning Distance Functions using Equivalence Relations
We address the problem of learning distance metrics using side-information in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a sim...
Aharon Bar-Hillel, Tomer Hertz, Noam Shental, Daph...