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» The Use of Classifiers in Sequential Inference
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RECOMB
2002
Springer
14 years 9 months ago
Monotony of surprise and large-scale quest for unusual words
The problem of characterizing and detecting recurrent sequence patterns such as substrings or motifs and related associations or rules is variously pursued in order to compress da...
Alberto Apostolico, Mary Ellen Bock, Stefano Lonar...
AIEDU
2005
185views more  AIEDU 2005»
13 years 8 months ago
A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory
The Bayesian framework offers a number of techniques for inferring an individual's knowledge state from evidence of mastery of concepts or skills. A typical application where ...
Michel C. Desmarais, Xiaoming Pu
DLOG
2010
13 years 6 months ago
TBox Classification in Parallel: Design and First Evaluation
Abstract. One of the most frequently used inference services of description logic reasoners classifies all named classes of OWL ontologies into a subsumption hierarchy. Due to emer...
Mina Aslani, Volker Haarslev
ICASSP
2011
IEEE
13 years 9 days ago
Real-time conjugate gradients for online fMRI classification
Real-time functional magnetic resonance imaging (rtfMRI) enables classification of brain activity during data collection thus making inference results accessible to both the subj...
Hao Xu, Yongxin Taylor Xi, Ray Lee, Peter J. Ramad...
NIPS
1997
13 years 10 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung