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» Using model knowledge for learning inverse dynamics
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172
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CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
15 years 9 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
122
Voted
AIED
2005
Springer
15 years 9 months ago
Generating Reports of Graphical Modelling Processes for Authoring and Presentation
Today's computer supported modelling environments could provide much more information about the users’ actions and problem solving processes than they usually store for late...
Lars Bollen
151
Voted
PRL
2000
182views more  PRL 2000»
15 years 3 months ago
Bayesian MLP neural networks for image analysis
We demonstrate the advantages of using Bayesian multi layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by c...
Aki Vehtari, Jouko Lampinen
145
Voted
FLAIRS
2008
15 years 6 months ago
Learning a Probabilistic Model of Event Sequences from Internet Weblog Stories
One of the central problems in building broad-coverage story understanding systems is generating expectations about event sequences, i.e. predicting what happens next given some a...
Mehdi Manshadi, Reid Swanson, Andrew S. Gordon
187
Voted
AROBOTS
2011
14 years 10 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox