  This paper studies the addition of linear constraints to the Support Vector
Regression (SVR) when the kernel is linear. Adding those constraints into the
problem allows to add prior knowledge on the estimator obtained, such as
finding probability vector or monotone data. We propose a generalization of the
Sequential Minimal Optimization (SMO) algorithm for solving the optimization
problem with linear constraints and prove its convergence. Then, practical
performances of this estimator are shown on simulated and real datasets with
different settings: non negative regression, regression onto the simplex for
biomedical data and isotonic regression for weather forecast.

Optimization and Control;Machine Learning