Academic Journal
Details on Gaussian Process Regression (GPR) and Semi-GPR Modeling
| العنوان: | Details on Gaussian Process Regression (GPR) and Semi-GPR Modeling |
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| المؤلفون: | Shekaramiz, Mohammad, Moon, Todd K., Gunther, Jacob H. |
| المصدر: | Electrical and Computer Engineering Faculty Publications |
| بيانات النشر: | Hosted by Utah State University Libraries |
| سنة النشر: | 2019 |
| المجموعة: | Utah State University: DigitalCommons@USU |
| مصطلحات موضوعية: | gaussian process regression, GPR, semi-GPR, modeling, Electrical and Computer Engineering |
| الوصف: | This report tends to provide details on how to perform predictions using Gaussian process regression (GPR) modeling. In this case, we represent proofs for prediction using non-parametric GPR modeling for noise-free predictions as well as prediction using semi-parametric GPR for noisy observations. |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| Relation: | https://digitalcommons.usu.edu/ece_facpub/216; https://digitalcommons.usu.edu/context/ece_facpub/article/1214/viewcontent/Details_on_Gaussian_Process_Regression_and_Semi_GPR_Modeling.pdf |
| DOI: | 10.13140/RG.2.2.17128.32002 |
| الاتاحة: | https://digitalcommons.usu.edu/ece_facpub/216 https://doi.org/10.13140/RG.2.2.17128.32002 https://digitalcommons.usu.edu/context/ece_facpub/article/1214/viewcontent/Details_on_Gaussian_Process_Regression_and_Semi_GPR_Modeling.pdf |
| Rights: | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact the Institutional Repository Librarian at digitalcommons@usu.edu. |
| رقم الانضمام: | edsbas.AD25257C |
| قاعدة البيانات: | BASE |
| DOI: | 10.13140/RG.2.2.17128.32002 |
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