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» Learning and using relational theories
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VMCAI
2012
Springer
14 years 3 months ago
A General Framework for Probabilistic Characterizing Formulae
Abstract. Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae o...
Joshua Sack, Lijun Zhang
PAMI
1998
86views more  PAMI 1998»
15 years 7 months ago
Spatial Sampling of Printed Patterns
—The bitmap obtained by scanning a printed pattern depends on the exact location of the scanning grid relative to the pattern. We consider ideal sampling with a regular lattice o...
Prateek Sarkar, George Nagy, Jiangying Zhou, Danie...
ICML
2007
IEEE
16 years 8 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
MLDM
2005
Springer
16 years 1 months ago
Unsupervised Learning of Visual Feature Hierarchies
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Fabien Scalzo, Justus H. Piater
206
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COLT
2001
Springer
16 years 2 days ago
Smooth Boosting and Learning with Malicious Noise
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...
Rocco A. Servedio