Zapis dokaza metode

Robust Gaussian Process

Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations.

Sources recorded, not reviewed

Izvorni zapis

Citati kopirani doslovno iz izvornog zapisa metode. Ne impliciraju nikakvu provjeru na razini tvrdnje.

Robust Gaussian Process Regression and Classification

Taksonomski zapis metode · ml-model / machine-learning

Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257. · URL
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. · ISBN 978-0-262-18253-9

Otvori cijelu metodu

Uređene tvrdnje

Tvrdnje pohranjene u knjigu dokaza, svaka s vlastitom procjenom.

Nema uređenih tvrdnji

Ovaj prikaz ne izmišlja procjenu tvrdnje kada knjiga dokaza nema nijednu.

Povezane metode

Generirano iz grafa metode i prikazano kao strojno predložene relacije — ne implicira se nikakva tvrdnja dokaza.

Taxonomic bucketBayesian Gaussian Processmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGaussian Processmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Linear Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Random Forestmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Support Vector Machinemachine-suggested · Relational suggestion, not evidence.