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NN
2010
Springer
189views Neural Networks» more  NN 2010»
13 years 2 months ago
Sparse kernel learning with LASSO and Bayesian inference algorithm
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is co...
Junbin Gao, Paul W. Kwan, Daming Shi
AAAI
2011
12 years 7 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
AIED
2005
Springer
14 years 1 months ago
Inferring learning and attitudes from a Bayesian Network of log file data
A student's goals and attitudes while interacting with a tutor are typically unseen and unknowable. However their outward behavior (e.g. problem-solving time, mistakes and hel...
Ivon Arroyo, Beverly Park Woolf
AAAI
2011
12 years 7 months ago
Mean Field Inference in Dependency Networks: An Empirical Study
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Daniel Lowd, Arash Shamaei
BMCBI
2010
229views more  BMCBI 2010»
13 years 7 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck