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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(5)
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scopus(18)
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: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: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:scopusEfficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers
ArticleAbstract: The well-known backpropagation learning algorithm is implemented in a field-programmable gate arrayPalabras claves:Embedded Systems, field-programmable gate array (FPGA), Hardware implementation, Microcontrollers, Supervised learningAutores:Francisco Ortega-Zamorano, Franco L., Jerez-Aragonés J.M., Luque-Baena R.M., Urda D.Fuentes:scopusHigh precision FPGA implementation of neural network activation functions
Conference ObjectAbstract: The efficient implementation of artificial neural networks in FPGA boards requires tackling severalPalabras claves:Autores:Francisco Ortega-Zamorano, Franco L., Jerez-Aragonés J.M., Juárez G.E., Perez J.O.Fuentes:scopusHybrid (generalization-correlation) method for feature selection in high dimensional DNA microarray prediction problems
Conference ObjectAbstract: Microarray data analysis is attracting increasing attention in computer science because of the manyPalabras claves:Constructive Neural Networks, Data Mining, DNA Microarray, feature selectionAutores:Couce Y., Franco L., Jerez-Aragonés J.M., José Luis Subirats, Urda D.Fuentes:scopusFPGA implementation comparison between C-mantec and back-propagation neural network algorithms
Conference ObjectAbstract: Recent advances in FPGA technology have permitted the implementation of neurocomputational models, mPalabras claves:Constructive Neural Networks, Fpga, Hardware implementationAutores:Francisco Ortega-Zamorano, Franco L., Jerez-Aragonés J.M., Juárez G.E.Fuentes:scopusFPGA implementation of the c-mantec neural network constructive algorithm
ArticleAbstract: Competitive majority network trained by error correction (C-Mantec), a recently proposed constructivPalabras claves:Circuit complexity, constructive neural networks (CoNN), on-chip learning, Threshold networksAutores:Francisco Ortega-Zamorano, Franco L., Jerez-Aragonés J.M.Fuentes:scopus