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  4. pyGPs - A Python Library for Gaussian Process Regression and Classification
 
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2015
Journal Article
Title

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.
Author(s)
Neumann, Marion  
Huang, S.
Marthaler, D.E.
Kersting, Kristian  
Journal
Journal of Machine Learning Research  
Link
Link
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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