Sökning: onr:"swepub:oai:DiVA.org:uu-432013" >
Approximate Gaussia...
Approximate Gaussian Process Regression and Performance Analysis Using Composite Likelihood
-
- Liu, Xiuming (författare)
- Uppsala universitet,Datorteknik,Embedded Systems
-
- Zachariah, Dave (författare)
- Uppsala universitet,Avdelningen för systemteknik
-
- Ngai, Edith (författare)
- Uppsala universitet,Datorteknik
-
(creator_code:org_t)
- IEEE, 2020
- 2020
- Engelska.
-
Ingår i: 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020, Espoo, Finland, September 21-24, 2020. - : IEEE. - 9781728166629 ; , s. 1-6
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Nonparametric regression using Gaussian Process (GP) models is a powerful but computationally demanding method. While various approximation methods have been developed to mitigate its computation complexity, few works have addressed the quality of the resulting approximations of the target posterior. In this paper we start from a general belief updating framework that can generate various approximations. We show that applying using composite likelihoods yields computationally scalable approximations for both GP learning and prediction. We then analyze the quality of the approximation in terms of averaged prediction errors as well as Kullback-Leibler (KL) divergences.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas