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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(6)
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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: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: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:scopusFPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Model
ArticleAbstract: A two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in aPalabras claves:Hardware implementation, Ising model, LFSR random number generator, Monte Carlo simulationsAutores:Cannas S.A., Francisco Ortega-Zamorano, Franco L., Jerez-Aragonés J.M., Montemurro M.A.Fuentes:scopusFPGA Implementation of Neurocomputational Models: Comparison Between Standard Back-Propagation and C-Mantec Constructive Algorithm
ArticleAbstract: 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:scopus