TY - JOUR
T1 - PyGPs - A python library for Gaussian process regression and classification
AU - Neumann, Marion
AU - Huang, Shan
AU - Marthaler, Daniel E.
AU - Kersting, Kristian
AU - Honkela, Antti
N1 - Publisher Copyright:
© 2015 Marion Neumann, Shan Huang, Daniel Marthaler, and Kristian Kersting.
PY - 2015/12
Y1 - 2015/12
N2 - 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.
AB - 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.
KW - Gaussian processes
KW - Python
KW - Regression and classification
UR - http://www.scopus.com/inward/record.url?scp=84961762867&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84961762867
SN - 1532-4435
VL - 16
SP - 2611
EP - 2616
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
ER -