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ICML
2005
IEEE
16 years 5 months ago
Reducing overfitting in process model induction
In this paper, we review the paradigm of inductive process modeling, which uses background knowledge about possible component processes to construct quantitative models of dynamic...
Will Bridewell, Narges Bani Asadi, Pat Langley, Lj...
126
Voted
NIPS
2003
15 years 6 months ago
Wormholes Improve Contrastive Divergence
In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model’s distribution. If the ...
Geoffrey E. Hinton, Max Welling, Andriy Mnih
ANOR
2011
175views more  ANOR 2011»
14 years 11 months ago
Integrated exact, hybrid and metaheuristic learning methods for confidentiality protection
A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentialit...
Fred Glover, Lawrence H. Cox, Rahul Patil, James P...
149
Voted
ICML
2003
IEEE
16 years 5 months ago
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning
In the task of adaptive information filtering, a system receives a stream of documents but delivers only those that match a person's information need. As the system filters i...
Yi Zhang, Wei Xu, James P. Callan
142
Voted
CVPR
2010
IEEE
16 years 4 hour ago
Multi-View Object Class Detection With a 3D Geometric Model
This paper presents a new approach for multi-view object class detection. Appearance and geometry are treated as separate learning tasks with different training data. Our approach...
Joerg Liebelt, Cordelia Schmid