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scopus(32)
A new constructive approach for creating all linearly separable (threshold) functions
Conference ObjectAbstract: A new constructive approach for creating all linearly separable functions is introduced. Balanced anPalabras claves:Autores:Anthony M., Franco L., Jerez-Aragonés J.M., José Luis SubiratsFuentes:scopusA new decomposition algorithm for threshold synthesis and generalization of Boolean functions
ArticleAbstract: A new algorithm for obtaining efficient architectures composed of threshold gates that implement arbPalabras claves:Circuit complexity, generalization, Linear separability, Logic synthesis, Threshold networksAutores:Franco L., Jerez-Aragonés J.M., José Luis SubiratsFuentes:scopusCommittee c-mantec: A probabilistic constructive neural network
Conference ObjectAbstract: C-Mantec is a recently introduced constructive algorithm that generates compact neural architecturesPalabras claves:Committee networks, Constructive Neural Networks, Supervised classificationAutores:Francisco Ortega-Zamorano, Franco L., Jerez-Aragonés J.M., José Luis Subirats, Luque-Baena R.M., Urda D.Fuentes:scopusC-Mantec: A novel constructive neural network algorithm incorporating competition between neurons
ArticleAbstract: C-Mantec is a novel neural network constructive algorithm that combines competition between neuronsPalabras claves:Active learning, Constructive neural network, Feed-forward network, generalization, Incremental learning, OverfittingAutores:Franco L., Jerez-Aragonés J.M., José Luis SubiratsFuentes:scopusConstructive neural networks to predict breast cancer outcome by using gene expression profiles
Conference ObjectAbstract: Gene expression profiling strategies have attracted considerable interest from biologist due to thePalabras claves:artificial neural networks, Breast Cancer, Constructive Neural Networks, Gene expression profiles, Pbkp_redictive modellingAutores:Franco L., Jerez-Aragonés J.M., José Luis Subirats, Urda D.Fuentes:scopusActive learning using a constructive neural network algorithm
Conference ObjectAbstract: Constructive neural network algorithms suffer severely from overfitting noisy datasets as, in generaPalabras claves:Autores:Franco L., Jerez-Aragonés J.M., José Luis Subirats, Molina I.Fuentes:scopusAddressing critical issues in the development of an Oncology Information System
ArticleAbstract: Purposes: This paper presents the experience on the design and implementation of a user-centered OncPalabras claves:Biomedical Applications, electronic health record, Oncology Information System, survival analysisAutores:Alba E., Franco L., Jerez-Aragonés J.M., José Luis Subirats, Ribelles N., Urda D.Fuentes:scopusAdvanced online survival analysis tool for predictive modelling in clinical data science
ArticleAbstract: One of the prevailing applications of machine learning is the use of predictive modelling in clinicaPalabras claves:Autores:Alba E., Franco L., Jerez-Aragonés J.M., José Luis Subirats, Montes-Torres J., Ribelles N., Urda D.Fuentes:scopusApplication of genetic algorithms and constructive neural networks for the analysis of microarray cancer data
ArticleAbstract: Background: Extracting relevant information from microarray data is a very complex task due to the cPalabras claves:Constructive Neural Networks, feature selection, Genetic Algorithms, MicroarrayAutores:Franco L., Jerez-Aragonés J.M., José Luis Subirats, Luque-Baena R.M., Urda D.Fuentes:scopusDeep neural network architecture implementation on FPGAs using a layer multiplexing scheme
Conference ObjectAbstract: In recent years predictive models based on Deep Learning strategies have achieved enormous success iPalabras claves:Deep Neural Networks, Fpga, Hardware implementation, Layer multiplexing, Supervised learningAutores:Francisco Ortega-Zamorano, Franco L., Gómez I., Jerez-Aragonés J.M.Fuentes:scopus