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
Bayesian Optimization 72 16.59%
Uncertainty Quantification 38 8.76%
GPR 37 8.53%
Active Learning 26 5.99%
Decision Making 18 4.15%
Model Predictive Control 14 3.23%
Computational Efficiency 11 2.53%
Classification 11 2.53%
Thompson Sampling 9 2.07%

Components


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

Categories