Non-Parametric Regression

Gaussian Process

Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.

Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
GPR 40 13.79%
Active Learning 24 8.28%
Time Series 23 7.93%
Decision Making 22 7.59%
Bayesian Optimisation 12 4.14%
Dimensionality Reduction 9 3.10%
Meta-Learning 8 2.76%
Experimental Design 7 2.41%
Density Estimation 5 1.72%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories