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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 ... -
Remainder subset awareness for feature subset selection
(Thomson Editores Spain, 2007)
Conference report
Open AccessFeature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. The family of algorithms known as plus-l-minus-r ... -
Feature selection in proton magnetic resonance spectroscopy for brain tumor classification
(2008)
Conference report
Open AccessH-MRS is a technique that uses response of protons under certain magnetic conditions to reveal the biochemical structure of human tissue. An important application is found in brain tumor diagnosis, due to the known ... -
Using fuzzy heterogeneous neural networks to learn a model of the central nervous system control
(Verlag Mainz, 1998)
Conference report
Open AccessFuzzy heterogeneous networks based on similarity are recently introduced feed-forward neural network models composed by neurons of a general class whose inputs are mixtures of continuous (crisp and/or fuzzy) with discrete ...
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