DocumentCode :
2779854
Title :
A Variational EM Approach to Predicting Uncertainty in Supervised Learning
Author :
Harva, Markus
Author_Institution :
Helsinki Univ. of Technol., Helsinki
fYear :
0
fDate :
0-0 0
Firstpage :
5342
Lastpage :
5346
Abstract :
In many applications of supervised learning, the conditional average of the target variables is not sufficient for prediction. The dependencies between the explanatory variables and the target variables can be much more complex calling for modelling the full conditional probability density. The ubiquitous problem with such methods is overfitting since due to the flexibility of the model the likelihood of any datapoint can be made arbitrarily large. In this paper a method for predicting uncertainty by modelling the conditional density is presented based on conditioning the scale parameter of the noise process on the explanatory variables. The regularisation problems are solved by learning the model using variational EM. Results with synthetic data show that the approach works well and experiments with real-world environmental data are promising.
Keywords :
expectation-maximisation algorithm; learning (artificial intelligence); probability; uncertainty handling; conditional probability density; supervised learning; uncertainty prediction; variational EM approach; Acoustic noise; Cost function; Function approximation; Multilayer perceptrons; Neural networks; Noise measurement; Predictive models; Supervised learning; Uncertainty; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.247312
Filename :
1716843
Link To Document :
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