Fast Gaussian process regression using representative data

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2001, Pages 132-137

  Yoshioka, T ^a   Ishii, S ^a  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; ESTIMATION; FUNCTIONS; GAUSSIAN NOISE (ELECTRONIC); LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES; REGRESSION ANALYSIS;

BAYESIAN NONPARAMETRIC REGRESSION MODEL; GAUSSIAN PROCESS REGRESSION; NEURAL NETWORK TRAINING;

NEURAL NETWORKS;

EID: 0034849788     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Sparse representation for Gaussian process models
2. Maximum likelihood from incomplete data via the EM algorithm
3. Variational Gaussian process classifiers
4. Efficient implementation of Gaussian processes
5. Bayesian learning for neural networks
6. On-line learning methods for Gaussian processes
7. Evaluation of Gaussian processes and other methods for non-linear regression
8. Sparse greedy Gaussian process regression
9. Gaussian processes for regression