Gaussian Processes in machine learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3176, Issue , 2004, Pages 63-71

  Rasmussen, Carl Edward ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTION FUNCTIONS; GAUSSIAN NOISE (ELECTRONIC); MACHINE LEARNING; RANDOM PROCESSES; REGRESSION ANALYSIS; STOCHASTIC SYSTEMS;

GAUSSIAN PROCESS REGRESSION MODEL; GAUSSIAN PROCESSES; HYPERPARAMETERS; MARGINAL LIKELIHOOD; TRAINING DATA;

GAUSSIAN DISTRIBUTION;

EID: 35048879177     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28650-9_4     Document Type: Article

References (5)

1. Williams, C.K.I.: Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In Jordan, M.I., ed.: Learning in Graphical Models. Kluwer Academic (1998) 599-621
2. MacKay, D.J.C.: Gaussian processes a replacement for supervised neural networks? Tutorial lecture notes for NIPS 1997 (1997)
3. Williams, C.K.I., Barber, D.: Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12) (1998) 1342-1351
4. Csato, L., Opper, M.: Sparse on-line Gaussian processes. Neural Computation 14 (2002) 641-668
5. Neal, R.M.: Regression and classification using Gaussian process priors (with discussion). In Bernardo, J.M., et al., eds.: Bayesian statistics 6. Oxford University Press (1998) 475-501