Gaussian Process Regression (GPR)
1. This code is based on the GPML toolbox V4.2.
2. Provided two demos (multiple input single output & multiple input multiple output).
3. Use feval(@ function name) to see the number of hyperparameters in a function. For example:
K > > feval (@ covRQiso)
Ans =
'(1 + 1 + 1)'
It shows that the covariance function covRQiso requires 3 hyperparameters. Therefore, 3
hyperparameters need to be initialized when using the optimization function minimize. The meaning
and range of each hyperparameter are explained in detail in the description of each function.
4. Different likelihood functions have different inference function requirements, which can be seen in
detail ./gpml-matlab-v4.2-2018-06-11/doc/index.html or ./gpml-matlab-v4.2-2018-06-
11/doc/manual.PDF.
MATLAB 릴리스 호환 정보
플랫폼 호환성
Windows macOS Linux카테고리
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Regression > Gaussian Process Regression >
태그
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!func
gpml-matlab-v4.2-2018-06-11
gpml-matlab-v4.2-2018-06-11/cov
gpml-matlab-v4.2-2018-06-11/doc
gpml-matlab-v4.2-2018-06-11/inf
gpml-matlab-v4.2-2018-06-11/lik
gpml-matlab-v4.2-2018-06-11/mean
gpml-matlab-v4.2-2018-06-11/prior
gpml-matlab-v4.2-2018-06-11/util
gpml-matlab-v4.2-2018-06-11/util/minfunc
gpml-matlab-v4.2-2018-06-11/util/minfunc/mex
gpml-matlab-v4.2-2018-06-11/util/sparseinv
GitHub 디폴트 브랜치를 사용하는 버전은 다운로드할 수 없음
버전 | 게시됨 | 릴리스 정보 | |
---|---|---|---|
1.0.0 |
|