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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jacobs, J.P. Koziel, S. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr., Univ. of Pretoria, Pretoria, South Africa (Jacobs, J.P.) || Sch. of Sci. & Eng., Reykjavik Univ., Reykjavik, Iceland (Koziel, S.) |
| Abstract | Gaussian process regression has been shown to be a highly effective tool for modeling the input characteristics of antennas. This study presents, for the first time, a rigorous comparison of two strategies for modeling $Re\{S_{11}\},$ $Im\{S_{11}\},$ and $|S_{11}|:$ the standard single-model method, and an approach that employs an ensemble of independent single models, one per equally-spaced frequency value in the range of interest. In spite of the fact that it uses far less training data, the singlemodel technique for the most approximately matched or even outdid the ensemble of GPR models in predictive performance - this appears to be due to the fact that the ensemble model disregards important covariance information regarding the latent function associated with the frequency dimension. |
| Starting Page | 510 |
| Ending Page | 513 |
| File Size | 400076 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781467357074 |
| DOI | 10.1109/ICEAA.2013.6632289 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-09-09 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Geometry Training Ground penetrating radar Predictive models Vectors Data models Dielectric resonator antennas |
| Content Type | Text |
| Resource Type | Article |
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