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Recent Submissions
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A deep learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques
(Springer, 2022)
Conference report
Restricted access - publisher's policyThere is increasing interest in the development of tools for investigating the protein ligand space. Understanding the underlying mechanisms of G protein-coupled receptors (GPCR) in the ligand-binding process is of particular ... -
The importance of interpretability and visualization in ML for medical applications
(2021)
Conference lecture
Open AccessMany areas of science have made a sharp transition towards data-dependent methods, enabled by simultaneous advances in data acquisition and the development of networked system technologies. This is particularly clear in ... -
The coming of age of interpretable and explainable machine learning models
(I6doc.com, 2021)
Conference report
Open AccessMachine learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm. In the wake of this realisation, strict legal regulations for these systems are currently being ... -
Off-the-grid: Fast and effective hyperparameter search for kernel clustering
(Springer, 2020)
Conference report
Open AccessKernel functions are a powerful tool to enhance the k-means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dramatic impact on the result. In supervised ... -
Fault detection and identification in a fuel cell system
(IOS Press, 2009)
Conference report
Restricted access - publisher's policyIn this work a fault diagnosis system for non-linear plants based on fuzzy logic, called VisualBlock-FIR, is presented and applied to an energy generation system based on fuel cells. VisualBlock-FIR runs under the Simulink ... -
Interpreting response to TMZ therapy in murine GL261 glioblastoma by combining Radiomics, Convex-NMF and feature selection in MRI/MRSI data analysis
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Conference report
Open AccessMachine learning (ML) methods have shown great potential for the analysis of data involved in medical decisions. However, for these methods to be incorpored in the medical pipeline, they must be made interpretable not only ... -
Similarity-based heterogeneous neuron models
(IOS Press, 2000)
Conference report
Open AccessThis paper introduces a general class of neuron models, accepting heterogeneous inputs in the form of mixtures of continuous (crisp or fuzzy) numbers, linguistic information, and discrete (either ordinal or nominal) ... -
Fuzzy inputs and missing data in similarity-based heterogeneous neural networks
(Springer, 1999)
Conference report
Open AccessFuzzy heterogeneous networks are recently introduced neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, ... -
On some strategies for missing values in positive semidefinite matrices
(Thompson, 2005)
Conference report
Open AccessThis article presents our work on missing values in Positive Semi-Definite or PSD matrices. We show how simple properties of PSD matrices can be used to deal with missing values. We study several situations and investigate ... -
A thermodynamic algorithm for feature selection
(Thomson Editores Spain, 2007)
Conference report
Open AccessThe main purpose of Feature Selection (FS) is to find a reduced subset of attributes from a data set described by a feature set. This implies a search process in the space of possible solutions, trying to optimize an ... -
Un algoritmo para el cálculo de la relevancia entrópica multivariada y su uso en la selección de variables
(Thomson Editores Spain, 2007)
Conference report
Open AccessLa reducción de la dimensionalidad mediante la selección de variables es uno de los pasos fundamentales del preprocesado de datos, como fase previa al análisis de información y descubrimiento de conocimiento. De entre los ... -
TFS: a thermodynamical search algorithm for feature subset selection
(Thomson Editores Spain, 2007)
Conference report
Open AccessThis work tackles the problem of selecting a subset of features in an inductive learning setting, by introducing a novel Thermodynamic Feature Selection algorithm (TFS). Given a suitable objective function, the algorithm ...
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