J
Juan José Rodríguez Diez
Researcher at University of Burgos
Publications - 6
Citations - 45
Juan José Rodríguez Diez is an academic researcher from University of Burgos. The author has contributed to research in topics: Boosting (machine learning) & Fault (power engineering). The author has an hindex of 4, co-authored 6 publications receiving 45 citations.
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Boosting Interval-Based Literals: Variable Length and Early Classification
TL;DR: A system for supervised time series classification, capable of learning from series of different length and able of providing a classification when only part of the series are presented to the classifier, and can be used to identify partial time series.
Book ChapterDOI
Learning Classification RBF Networks by Boosting
TL;DR: This work proposes a novel method for constructing RBF networks, based on boosting, where the task assigned to the base learner is to select a RBF, while the boosting algorithm combines linearly the different RBFs.
Journal ArticleDOI
Diagnosis of continuous dynamic systems: integrating consistency based diagnosis with machine-learning techniques
Belarmino Pulido,Juan José Rodríguez Diez,Carlos Alonso González,Oscar J. Prieto,Esteban R. Gelso +4 more
TL;DR: In this article, an integrated approach to diagnosis of complex dynamic systems, combining model based diagnosis with machine learning techniques, is proposed, and a simple framework to make them cooperate, hence improving the diagnosis capabilities of each individual method.
Book ChapterDOI
Building RBF Networks for Time Series Classification by Boosting
TL;DR: This work presents a learning system for the classification of multivariate time series that is useful in domains such as biomedical signals, continuous systems diagnosis, or data mining in temporal databases.
Proceedings ArticleDOI
Focusing fault localization in model-based diagnosis with case-based reasoning
Anibal Bregon,Belarmino Pulido,M. Aranzazu Simon,Isaac Moro,Oscar J. Prieto,Juan José Rodríguez Diez,Carlos J. Alonso-González +6 more
TL;DR: This work study the combination of a consistency-based diagnosis system together with a Case-based Reasoning system that will perform fault detection and localization and the CBR system provides accurate indication of the most probable fault mode, at early stages of the localization process.