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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
ISBN:
9780791859599
No. of Pages:
686
Publisher:
ASME Press
Publication date:
2010
Citation
Qi, Q, & Shang, Y. "Comparing Probabilistic Graphical Model Based and Gaussian Process Based Selections for Predicting the Temporal Observations." Intelligent Engineering Systems through Artificial Neural Networks, Volume 20. Ed. Dagli, CH. ASME Press, 2010.
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In wireless sensor networks, the limited power source makes sensing expensive. It is thus a big optimization problem that obtaining more and useful information but making less observations. In this paper, we compare two model based approaches. One is to apply the improved VoIDP algorithm on a chain graphical model for selecting a subset of observations that minimizes the overall uncertainty; The other is to find a selection of observations on a Gaussian process model that maximizes the entropy and the mutual information criteria respectively. We compare the selections based on their prediction accuracies for the temporal observations on a...
Abstract
I. Introduction
II. Probabilistic Graphical Model Based Selection
III. Gaussian Process Based Selection
IV. Experiments
V. Conclusions
References
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