arXiv Open Access 2014

Scalable Variational Gaussian Process Classification

James Hensman Alex Matthews Zoubin Ghahramani

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Abstrak

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.

Topik & Kata Kunci

stat.ML

Penulis (3)

James Hensman

Alex Matthews

Zoubin Ghahramani

Format Sitasi

APA MLA BibTeX

Hensman, J., Matthews, A., Ghahramani, Z. (2014). Scalable Variational Gaussian Process Classification. https://arxiv.org/abs/1411.2005

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Informasi Jurnal

Tahun Terbit: 2014
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓