Gaussian Processes for Machine Learning
GPTKB entity
Properties (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
software
|
| gptkbp:developedBy |
Carl Edward Rasmussen
Christopher_K._I._Williams |
| https://www.w3.org/2000/01/rdf-schema#label |
Gaussian Processes for Machine Learning
|
| gptkbp:isAvenueFor |
Optimization
Spatial Statistics Time_Series_Analysis |
| gptkbp:isBasedOn |
Bayesian Inference
|
| gptkbp:isChallengedBy |
Scalability Issues
Computational Complexity Choice of Kernel |
| gptkbp:isCharacterizedBy |
Kernel Functions
Covariance Functions Mean Functions |
| gptkbp:isDocumentedIn |
Research Papers
Textbooks Documentation Online Tutorials |
| gptkbp:isEvaluatedBy |
Cross-Validation
Posterior Predictive Checks Log Marginal Likelihood |
| gptkbp:isInfluencedBy |
Bayesian Statistics
Statistical Learning Theory Non-parametric Statistics |
| gptkbp:isKnownFor |
Flexibility
Ability_to_Model_Complex_Functions Non-parametric_Nature |
| gptkbp:isLocatedIn |
MATLAB
Python R |
| gptkbp:isPopularIn |
Artificial Intelligence
Statistics Data_Science |
| gptkbp:isRelatedTo |
gptkb:Support_Vector_Machines
Markov Chain Monte Carlo Neural Networks |
| gptkbp:isSupportedBy |
Gaussian Process Regression
Gaussian_Process_Classification Gaussian_Process_Latent_Variable_Models |
| gptkbp:isTaughtIn |
Statistics Courses
Machine Learning Courses Data_Science_Programs |
| gptkbp:isUsedFor |
Anomaly Detection
Active Learning Bayesian Optimization Surrogate Modeling |
| gptkbp:isUsedIn |
Classification
Regression |
| gptkbp:provides |
Uncertainty Quantification
|
| gptkbp:publishedIn |
2006
|
| gptkbp:requires |
Computational_Resources
Hyperparameter_Tuning |