Regression

Standard regression problems

In a regression problem, we would typically have some input vectors x and some desired output values y. Note that, differently from classification problems, here the output values y are not restricted to be class labels, but can rather be continuous variables or vectors.

Models

Linear Regression

See Simple Linear Regression

Multivariate Linear Regression

See Multivariate Linear Regression

Multiple Linear Regression

See Multiple Linear Regression and Partial Least Squares

Logistic Regression

See Logistic regression, Logistic Regression Analysis and Generalized Linear Models.

Multinomial Logistic Regression (Softmax)

See Multinomial Logistic Regression.

Support Vector Machines

See Sequential Minimal Optimization for Regression, L1-regularized logistic regression, L2-regularized logistic regression in the dual and L2-regularized L2-loss logistic regression.

Neural Networks

See Levenberg-Marquardt with Bayesian Regularization and Resilient Backpropagation.

Variations

Regression models censored in time

See Cox's Proportional Hazards Model