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Regularized Gaussian Process

A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.

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Regularized Gaussian Process Regression and Classification

Taxonomický záznam metódy · ml-model / machine-learning

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. · ISBN 978-0-262-18253-9
Scholkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press. · ISBN 978-0-262-19475-4

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Taxonomic bucketBayesian Gaussian Processmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGaussian Processmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRegularized linear regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRegularized Support Vector Machinemachine-suggested · Relational suggestion, not evidence.