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2014 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2014 - Proceedings(1)
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scopus(14)
Compactly supported graph building for spectral clustering
Conference ObjectAbstract: In spectral clustering approaches is of great importance how is built the graph representation overPalabras claves:Autores:A. E. Castro-Ospina, Álvarez-Meza A.M., Castellanos-Dominguez G.Fuentes:scopusDevelopments on solutions of the normalized-cut-clustering problem without eigenvectors
Conference ObjectAbstract: Normalized-cut clustering (NCC) is a benchmark graph-based approach for unsupervised data analysis.Palabras claves:Eigenvectors, Graph-based clustering, Normalized cut clustering, Quadratic formsAutores:A. E. Castro-Ospina, Diego Hernán Peluffo-Ordóñez, Israel D. Herrera-Granda, K. L. Ponce-Guevara, Leandro L. Lorente-Leyva, Miguel A. Becerra, Paul D. Rosero-Montalvo, Rodríguez-Sotelo J.L.Fuentes:scopusEfficient hyperparameter optimization in convolutional neural networks by learning curves prediction
Conference ObjectAbstract: In this work, we present an automatic framework for hyperparameter selection in Convolutional NeuralPalabras claves:deep learning, forecasting, Hyperparameter optimization, Learning curves, Singular spectrum analysis, SVRAutores:A. E. Castro-Ospina, Cardona-Escobar A., Giraldo-Forero A., Jaramillo-Garzon J.A.Fuentes:scopusFeature group selection using mkl penalized with ℓ<inf>1</inf> -norm and svm as base learner
Conference ObjectAbstract: Objective feature selection is an important component in the machine learning framework, which has aPalabras claves:feature selection, Group LASSO, Multimodality, multiple kernel learning, sparsityAutores:A. E. Castro-Ospina, Areiza-Laverde H.J., Díaz G.M.Fuentes:scopusInformation quality assessment for data fusion systems
ArticleAbstract: This paper provides a comprehensive description of the current literature on data fusion, with an emPalabras claves:Context assessment, data fusion, information quality, Quality assessmentAutores:A. E. Castro-Ospina, Diego Hernán Peluffo-Ordóñez, Miguel A. Becerra, Tobon C.Fuentes:scopusProtein fold families prediction based on graph representations and machine learning methods
Conference ObjectAbstract: Prediction of protein fold families remains an existing challenge in molecular biology and bioinformPalabras claves:Autores:A. E. Castro-Ospina, Areiza-Laverde H.J., Jaramillo-Garzon J.A., Mercado-Diaz L.R.Fuentes:scopusMulti-label learning by hyperparameters calibration for treating class imbalance
Conference ObjectAbstract: Multi-label learning has been becoming an increasingly active area into the machine learning communiPalabras claves:Imbalanced learning, Multilabel classification, Problem transformation, Support Vector MachineAutores:A. E. Castro-Ospina, Cardona-Escobar A., Giraldo-Forero A.Fuentes:scopusMulti-labeler classification using kernel representations and mixture of classifiers
Conference ObjectAbstract: This work introduces a multi-labeler kernel novel approach for data classification learning from mulPalabras claves:Multi-labeler classification, Supervised kernel, SUPPORT VECTOR MACHINESAutores:A. E. Castro-Ospina, Arciniegas-Mejía A.F., Bolaños-Ledezma M., Bravo-Montenegro M.J., Diego Hernán Peluffo-Ordóñez, Guasmayan-Guasmayan F.A., Gustin I.D., Imbajoa-Ruiz D.E.Fuentes:scopusMultiple kernel learning for spectral dimensionality reduction
Conference ObjectAbstract: This work introduces a multiple kernel learning (MKL) approach for selecting and combining differentPalabras claves:Dimensionality reduction, Generalized kernel, Kernel PCA, multiple kernel learningAutores:A. E. Castro-Ospina, Alvarado-PÉrez J.C., Diego Hernán Peluffo-Ordóñez, Javier E. Revelo-FuelagánFuentes:scopusOn the relationship between dimensionality reduction and spectral clustering from a kernel viewpoint
Conference ObjectAbstract: This paper presents the development of a unified view of spectral clustering and unsupervised dimensPalabras claves:Dimensionality reduction, Generalized kernel formulation, Kernel PCA, Spectral clustering, Support Vector MachineAutores:A. E. Castro-Ospina, Alvarado-PÉrez J.C., Anaya-Isaza A.J., Diego Hernán Peluffo-Ordóñez, Miguel A. Becerra, Therón R., Xiomara P. Blanco-ValenciaFuentes:scopus