Scalable Variational Gaussian Process Classification

Autor:	Hensman, James, Matthews, Alex, Ghahramani, Zoubin
Rok vydání:	2014
Předmět:	Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments. Comment: 16 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1411.2005 Zobrazit plný text záznamu View this record from Arxiv