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FLAIRS
2006
13 years 9 months ago
Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning
A major difficulty in building Bayesian network models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with th...
Adam Zagorecki, Mark Voortman, Marek J. Druzdzel
DICTA
2003
13 years 9 months ago
Learning Semantic Concepts from Visual Data Using Neural Networks
For content-based image retrieval techniques, query image is used to pick up and rank some relevant images from a database using some certain similarity metric. If semantic feature...
Xiaohang Ma, Dianhui Wang
ICML
2008
IEEE
14 years 8 months ago
On-line discovery of temporal-difference networks
We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three criteria to expand a node in TD...
Takaki Makino, Toshihisa Takagi
UAI
2000
13 years 9 months ago
Tractable Bayesian Learning of Tree Belief Networks
In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tracta...
Marina Meila, Tommi Jaakkola
LCN
2006
IEEE
14 years 1 months ago
Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks
Literature on the use of machine learning (ML) algorithms for classifying IP traffic has relied on fullflows or the first few packets of flows. In contrast, many real-world scenar...
Thuy T. T. Nguyen, Grenville J. Armitage