Abstract
The main reason why the nonparametric prediction using Gaussian processes has not been popular for resource-constrained multi-agent systems is the fact that the optimal prediction must use all cumulatively measured values in a non-trivial way [74, 75].
Keywords
- Gaussian Process
- Target Point
- Communication Range
- Gradient Descent Algorithm
- Gaussian Process Regression
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 The Author(s)
About this chapter
Cite this chapter
Xu, Y., Choi, J., Dass, S., Maiti, T. (2016). Memory Efficient Prediction With Truncated Observations. In: Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-21921-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-21921-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21920-2
Online ISBN: 978-3-319-21921-9
eBook Packages: EngineeringEngineering (R0)