Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
Robert Tibshirani and Trevor Hastie and Jerome Friedman

Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies.pdf 43.19kB
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@article{9:10,author={Jerome Friedman and Trevor Hastie and Robert Tibshirani}, Title={Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/freund08a/freund08a.pdf}}
Citation:
Tibshirani, R., Hastie, T., & Friedman, J.. (2014). Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies [Data set]. Academic Torrents. https://academictorrents.com/details/87f561d3d72af9b20f9d0298efddb88f948591db
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