Work

pyGPs - A Python Library for Gaussian Process Regression and Classification

Year: 2015

Type: article

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.

Source: Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)

Authors: Neumann, M., Huang, S., Marthaler, D.E., Kersting, K.

Institutions: Washington University in St. Louis, Fraunhofer Institute for Intelligent Analysis and Information Systems, TU Dortmund University

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2277766262

FWCI: 2.063

Cites: 10

Cited by: 22

Related to: 10