Sparse matrix regression (SMR) is a two-dimensional supervised feature selection method that can directly select the features on matrix data. It uses several couples of left and right regression ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

A model for two-dimensional linear iterative circuits is defined in the form of matrix equations. From the matrix equations, a two-dimensional characteristic function is defined. It is then proved ...

The final predictions are the sum of the raw linear predictions and the residuals modeled by the Random Forest. Linear Boosting is a two stage learning process. Firstly, a linear model is trained on ...

HiGHS is a high performance serial and parallel solver for large scale sparse linear optimization problems of the form $$ \min \quad \dfrac {1} {2}x^TQx + c^Tx \qquad \textrm {s.t.}~ \quad L \leq Ax ...