pyGPs -- a Python Library for Gaussian Process Regression and Classification
Abstract
We introduce pyGPs, an object-oriented implementation of Gaussian processes (gps) for machine learning. The library provides a wide range of functionalities reaching from simple gp specification via mean and covariance and gp inference to more complex implementations of hyperparameter optimization, sparse approximations, and graph based learning. Using Python we focus on usability for both "users" and "researchers". Our main goal is to offer a user- friendly and flexible implementation of gps for machine learning.
Cite
Text
Neumann et al. "pyGPs -- a Python Library for Gaussian Process Regression and Classification." Machine Learning Open Source Software, 2015.Markdown
[Neumann et al. "pyGPs -- a Python Library for Gaussian Process Regression and Classification." Machine Learning Open Source Software, 2015.](https://mlanthology.org/mloss/2015/neumann2015jmlr-pygps/)BibTeX
@article{neumann2015jmlr-pygps,
title = {{pyGPs -- a Python Library for Gaussian Process Regression and Classification}},
author = {Neumann, Marion and Huang, Shan and Marthaler, Daniel E. and Kersting, Kristian},
journal = {Machine Learning Open Source Software},
year = {2015},
pages = {2611-2616},
volume = {16},
url = {https://mlanthology.org/mloss/2015/neumann2015jmlr-pygps/}
}