Examinando por Materia "Entropy"
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Ítem An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data(SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoUnsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.Ítem An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data(SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.Ítem An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data(SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y ConservaciónUnsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.Ítem Entropy-based graph construction methods for unsupervised data structure detection(Universidad EAFIT, 2021) Ariza Jiménez, Leandro Fabio; Quintero Montoya, Olga Lucía; Pinel Peláez, NicolásÍtem Modelo matemático combinado para la clasificación de neuroimágenes basado en medidas de similaridad entre hemisferios del cerebro(Universidad EAFIT, 2020) Cardona Pineda, Danny Styvens; Laniado Rodas, HenryThe main contribution of this work is the combination of similarity measures, methods for the construction of subspaces and classification models. Specifically, the NCC was used as a measure of similarity, which was projected to subspace in singular value decomposition following the Eigenfaces methodology, to then apply classification models on these projections. Results with an accuracy of 81% and a predictive capacity of at least 79% were observed for this combination of methods.Ítem Over the non-extensivity parameter for some superadditives systems(Universidad EAFIT, 2010-06-01) Borja–Tamayo, R.; Cartagena Marín, C.; Loaiza Ossa, Gabriel Ignacio; Molina Vélez, G.; Puerta Yepes, María Eugenia; Universidad EAFITÍtem Sobre estructuras geométricas para modelos q-Exponenciales(Universidad EAFIT, 2014) Restrepo Zuleta, Marinela Andrea; Carvajal Fernández, Leonardo Fabio; Loaiza Ossa, Gabriel Ignacio