Antonio Álvarez-Vellisco

TL;DR: A methodology that sinergically combines a select of successful advances in each step to automatically classify microcalcifications in digitized mammography and uses a Neural feature selector and detector to finally classify the MCs.

TL;DR: Artificial Metaplasticity is a novel Artificial Neural Network training algorithm that gives more relevance to less frequent training patterns and subtracts relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the MLP performance.

TL;DR: In this article, the results of four years of a research aimed at carrying out a comparative analysis between the application of the European Credit Transfer and Accumulation System (ECTS) and the traditional teaching and learning system (TTLS) to first-year students, in order to improve their performance in the subject Analysis of Circuits I (AC-I) are presented.

TL;DR: This chapter is dedicated to scope of the application of Importance Sampling Techniques to the design phase of Neyman-Pearson Neural Detectors.