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» Derivation of Knowledge Structures for Distributed Learning ...
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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...
117
Voted
ACL
2010
14 years 10 months ago
Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However,...
Partha Pratim Talukdar, Fernando Pereira
105
Voted
COLT
1994
Springer
15 years 4 months ago
Rigorous Learning Curve Bounds from Statistical Mechanics
In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over ...
David Haussler, H. Sebastian Seung, Michael J. Kea...
104
Voted
CVPR
2003
IEEE
16 years 2 months ago
A Bayesian Framework for Fusing Multiple Word Knowledge Models in Videotext Recognition
Videotext recognition is challenging due to low resolution, diverse fonts/styles, and cluttered background. Past methods enhanced recognition by using multiple frame averaging, im...
DongQing Zhang, Shih-Fu Chang
116
Voted
FOCS
1990
IEEE
15 years 4 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum