Machine learningMachine learning
Gaussian Process
A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.
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Sources
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
- Gaussian process. Wikipedia. link ↗
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Active learning Gaussian processBayesian Active LearningBayesian Decision TreeBayesian Federated LearningBayesian Few-Shot LearningBayesian Gaussian Mixture ModelBayesian Gaussian ProcessBayesian Metric LearningBayesian Naive BayesBayesian Nonparametric MethodsBayesian one-class SVMBayesian Online LearningBayesian Random ForestBayesian Semi-supervised LearningBayesian Stacking EnsembleBayesian Support Vector MachineEnsemble Gaussian ProcessExplainable Gaussian ProcessMetric LearningRegularized Gaussian ProcessRegularized k-nearest neighborsRegularized semi-supervised learningRobust Gaussian ProcessSelf-supervised Gaussian ProcessSelf-supervised One-class SVMSemi-supervised Gaussian ProcessSemi-supervised One-class SVMSpatial Variational Inference