A modeling-based classification algorithm validated with simulated data

TL;DR: In this article, the structure theory of linear operators on finite-dimensional vector spaces has been studied and a self-contained treatment of that subject is given, along with a discussion of the relations between dynamical systems and certain fields outside pure mathematics.

TL;DR: A popular SVM implementation is compared to 16 classification methods and 9 regression methods accessible through the software R by the means of standard performance measures and bias-variance decompositions which showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.

TL;DR: The combination of classifiers leads to substantial reduction of misclassification error in a wide range of applications and benchmark problems and the procedure performs comparable to the best classifiers used in a number of artificial examples and applications.