Journal ArticleDOI
Neural networks
Alberto Prieto,Beatriz Prieto,Eva M. Ortigosa,Eduardo Ros,Francisco J. Pelayo,Julio Ortega,Ignacio Rojas +6 more
TLDR
The development and evolution of different topics related to neural networks is described showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems.About:
This article is published in Neurocomputing.The article was published on 2016-11-19. It has received 184 citations till now. The article focuses on the topics: Neural modeling fields & Nervous system network models.read more
Citations
More filters
Journal ArticleDOI
Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
TL;DR: The proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database and can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
Journal ArticleDOI
Recommendation system based on deep learning methods: a systematic review and new directions
TL;DR: This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications and indicated that autoencoder models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks and the Recurrent Neural Networks.
Journal ArticleDOI
Deep learning approach for microarray cancer data classification
TL;DR: A deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes using a 7-layer deep neural network architecture having various parameters for each dataset is developed.
Journal ArticleDOI
Supervised learning in spiking neural networks: A review of algorithms and evaluations
TL;DR: This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively, and provides five qualitative performance evaluation criteria and presents a new taxonomy for supervisedLearning algorithms depending on these five performance evaluated criteria.
Journal ArticleDOI
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
TL;DR: A thorough review on the development of ML-ELMs, including stacked ELM autoencoder, residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications, and the connection between random neural networks and conventional deep learning.
References
More filters
Journal ArticleDOI
A reconfigurable neuroprocessor for self-organizing feature maps
TL;DR: A scalable FPGA-based hardware accelerator for the emulation of Self-Organizing Feature Maps (SOMs) with a multi-threaded software implementation on a state-of-the-art multi-core microprocessor is compared.
Journal ArticleDOI
Improving the tolerance of multilayer perceptrons by minimizing the statistical sensitivity to weight deviations
TL;DR: The modified backpropagation algorithm proposed uses the statistical sensitivity of the network to changes in the weights as a quantitative measure of network tolerance and attempts to reduce this statistical sensitivity while keeping the figures for the usual training performance similar to those obtained with the usual backpropAGation algorithm.
Posted Content
Credit risk prediction: A comparative study between discriminant analysis and the neural network approach
TL;DR: In this paper, the authors explore a new practical way based on the Neural Networks that would help the banker to predict the non payment risk the companies asking for a loan, motivated by the insufficiency of traditional prevision models.
Book ChapterDOI
Liquid Computing
TL;DR: This article describes liquid computing as a new framework for analyzing those types of computations that are usually carried out in biological organisms - either in the brain, or in the form of gene regulation within a single cell.
Implementation of a Biologically Realistic Parallel Neocortical-Neural Network Simulator.
TL;DR: The primary goal of this simulator is to create a novel classi er based on a biologically realistic neocortical-neural network, which is accomplished by modeling a whole community of cells within a brain structure and observing the emergent behavior of this system.