Validation-Based Sparse Gaussian Process Classifier Design


Autoria(s): Shevade, Shirish; Sundararajan, S
Data(s)

01/07/2009

Resumo

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/21109/1/neco.2009.pdf

Shevade, Shirish and Sundararajan, S (2009) Validation-Based Sparse Gaussian Process Classifier Design. In: Neural Computation, 21 (7). pp. 2082-2103.

Publicador

MIT Pres

Relação

http://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.03-08-724

http://eprints.iisc.ernet.in/21109/

Palavras-Chave #Computer Science & Automation (Formerly, School of Automation)
Tipo

Journal Article

PeerReviewed