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» Learning Distance Functions using Equivalence Relations
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TNN
2010
216views Management» more  TNN 2010»
13 years 4 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok
LREC
2008
104views Education» more  LREC 2008»
13 years 11 months ago
Learning Patterns for Building Resources about Semantic Relations in the Medical Domain
In this article, we present a method for extracting automatically from texts semantic relations in the medical domain using linguistic patterns. These patterns refer to three leve...
Mehdi Embarek, Olivier Ferret
ICML
2007
IEEE
14 years 10 months ago
Information-theoretic metric learning
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit ...
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
14 years 10 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
AAAI
2006
13 years 11 months ago
Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping
Transfer learning concerns applying knowledge learned in one task (the source) to improve learning another related task (the target). In this paper, we use structure mapping, a ps...
Yaxin Liu, Peter Stone