Non-Parametric Classification

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
regression 82 14.16%
Bayesian Optimization 81 13.99%
Uncertainty Quantification 43 7.43%
GPR 34 5.87%
Active Learning 24 4.15%
Decision Making 19 3.28%
Computational Efficiency 18 3.11%
Model Predictive Control 13 2.25%
Dimensionality Reduction 11 1.90%

Components


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

Categories