Regularization algorithm and its implementation in general systems theory and its implications in physics

In recent years a new physics known as complexity has emerged, whose fundamental structure is based on the general theory of systems or the so-called mathematical biology. There, problems arise with a large number of variables on which decisions must be made, and it is important to have selection techniques that are robust and efficient, In this research work, a couple of techniques are presented that will serve the aforementioned purpose; these are: the Lasso regularized linear regression technique and the Ridge regression technique, the latter with the slight difference in the penalty, since it makes use of a different rule than the Euclidean one, which implies very different consequences. One of these techniques was applied in a clinical problem and corresponds to the study of patients with diabetes. With this objective, the algorithm of descent of coordinates was implemented for the type of regularization loop and the analytical solution of regression of ridges. In addition, Kernels was used for the implementation of the regularized vector machine. The above algorithms were compared with the Ridge regularization and the Euclidean regularized vector machine. These types of techniques are common in physics, and it is vitally important to be able to implement other types of rules for a problem in which the choice of them can be simplified.