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
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Journal ArticleDOI
Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities
TL;DR: Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings, and the most accurate model was produced by Random forest method.
Journal ArticleDOI
An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking
Madjid Tavana,Madjid Tavana,Amir-Reza Abtahi,Debora Di Caprio,Debora Di Caprio,Maryam Poortarigh +5 more
TL;DR: A model that uses Artificial Neural Networks and Bayesian Networks to modeling ambiguous occurrences related to bank liquidity risk measurement is proposed and a real-world case study is presented to demonstrate applicability and exhibit the efficiency, accuracy and flexibility of data mining methods.
Journal ArticleDOI
A rapid screening and regrouping approach based on neural networks for large-scale retired lithium-ion cells in second-use applications
TL;DR: Two rapid and accurate screening approaches are proposed to address the problem of low efficiency and low accuracy of large-scale retired cells by constructing two novel screening models using the capacity and voltage profiles of a small number of sample cells.
Journal ArticleDOI
Multi-cue fusion for emotion recognition in the wild
TL;DR: This work proposes a multi-cue fusion emotion recognition (MCFER) framework by modeling human emotions from three complementary cues, i.e., facial texture, facial landmark action and audio signal, and then fusing them together.
Journal ArticleDOI
A survey of neural network-based cancer prediction models from microarray data.
Maisa Daoud,Michael Mayo +1 more
TL;DR: Results indicate that the functionality of the neural network determines its general architecture, however, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.