JAPÓN SÁEZ , Alberto

The goals of the work are twofold. The first one is about the background of mathematics of pursuit and evasion, models that are known as Chases and Escapes games. Five problems are solved using clasic techniques from Mathematic Analysis, and a sixth one of a different nature where its solution is described from a probabilistic point of view....

This thesis dissertation focus on developing new approaches for different Data Science problems from a Location Theory perspective. In particular, we concentrate on locating hyperplanes by means of solving Mixed Integer Linear and Non Linear Problems. Chapter 1 introduces the baseline techniques involved in this work, which encompass Support...

In this paper we propose novel methodologies to optimally construct Support Vector Machine-based classifiers that take into account that label noise occur in the training sample. We propose different alternatives based on solving Mixed Integer Linear and Non Linear models by incorporating decisions on relabeling some of the observations in the...

In this paper, we present a novel approach to construct multiclass classifiers by means of arrangements of hyperplanes. We propose different mixed integer (linear and non linear) programming formulations for the problem using extensions of widely used measures for misclassifying observations where the kernel trick can be adapted to be...

In this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. The motivation of this new methodology is based on the superaditive effect of combining together margin based classifiers and outlier detection techniques. Our approach rests on two...