methods_list.m

% ------------------------------
% METHODS
% ------------------------------

% LINEAR MODELS
%    * Regularized Least squares Linear regression (RLR) 
%    * Least Absolute Shrinkage and Selection Operator (LASSO).
%    * Elastic Net (ELASTICNET).

% SPLINES and POLYNOMIALS
%    * Adaptive Regression Splines (ARES)
%    * Locally Weighted Polynomials (LWP)

% NEIGHBORS
%    * k-nearest neighbors regression (KNNR)
%    * Weighted k-nearest neighbors regression (WKNNR)

%   TREE MODELS
%    * Decision trees (TREE)
%    * Bagging trees (BAGTREE)
%    * Boosting trees (BOOST)
%    * Random forests (RF1)
%    * Boosting random trees (RF2)

% NEURAL NETWORS
%    * Neural networks (NN)
% ---* RBF Neural networks (NN)
%    * Extreme Learning Machines (ELM)

% KERNEL METHODS
%    * Support Vector Regression (SVR)
%    * Kernel Ridge Regression (KRR), aka Least Squares SVM
%    * Relevance Vector Machine (RVM)
%    * Kernel signal to noise regression (KSNR)
%    * Structured KRR (SKRR)
%    * Random Kitchen Sinks Regression (RKS)
%
% % GAUSSIAN PROCESSES
%    * Gaussian Process Regression (GPR)
%    * Variational Heteroscedastic Gaussian Process Regression (VHGPR)
%    * Warped Gaussian Processes (WGPR)
%    * Sparse Spectrum Gaussian Process Regression (SSGPR)
%    * Twin Gaussian Processes (TGP)