Publikationen, an denen er mitarbeitet JOSE IGNACIO MARTIN ARAMBURU (41)

2017

  1. A Stress Classification System Based on Arousal Analysis of the Nervous System

    Biosystems and Biorobotics (Springer International Publishing), pp. 783-787

  2. A real-time stress classification system based on arousal analysis of the nervous system by an F-state machine

    Computer Methods and Programs in Biomedicine, Vol. 148, pp. 81-90

2015

  1. Analysis of introducing active learning methodologies in a basic computer architecture course

    IEEE Transactions on Education, Vol. 58, Núm. 2, pp. 110-116

2014

  1. A hierarchical BCI system able to discriminate between Non intentional Control state and four Intentional Control activities

    PhyCS 2014 - Proceedings of the International Conference on Physiological Computing Systems

2013

  1. Aprendizaje cooperativo y basado en proyectos en la asignatura Arquitectura de Computadores

    ReVisión, Vol. 6, Núm. 2

  2. Game-console-based projects for learning the computer input/output subsystem

    IEEE Transactions on Education, Vol. 56, Núm. 4, pp. 453-458

  3. Una experiencia en el uso de metodologías activas en la asignatura Arquitectura de Computadores

    Actas de las XIX Jornadas de la Enseñanza Universitaria de la Informática: JENUI 2013 : Castellón, del 10 al 12 de julio de 2013

2012

  1. Nintendo® DS projects to learn computer input-output

    Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE

2011

  1. Towards a standard methodology to evaluate internal cluster validity indices

    Pattern Recognition Letters, Vol. 32, Núm. 3, pp. 505-515

2010

  1. Consolidated trees versus bagging when explanation is required

    Computing (Vienna/New York), Vol. 89, Núm. 3-4, pp. 113-145

  2. Obtaining optimal class distribution for decision trees: Comparative analysis of CTC and C4.5

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index

    Pattern Recognition, Vol. 43, Núm. 10, pp. 3364-3373

2009

  1. SIHC: A STABLE INCREMENTAL HIERARCHICAL CLUSTERING ALGORITHM

    ICEIS 2009 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS