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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Book ChapterDOI
Searching the Sky for Neural Networks
TL;DR: This paper focuses on the semantic discovery of N2Sky services through a human-centered querying mechanism termed as N2Query, which delivers a list of ranked neural network services to the user as a solution to their stated problem.
Proceedings ArticleDOI
A Wilcoxon Norm Based Robust Machine Learning Approach for Traffic Noise Prediction
TL;DR: It is found that Wilcoxon norm based artificial neural network model (WNN) has best performance with the presence of outlier compare to conventional multilayer perceptron neural network.
Journal ArticleDOI
Mechanical Properties, Failure Mechanisms, and Scaling Laws of Bicontinuous Nanoporous Metallic Glasses
TL;DR: In this article , the authors used genetic programming to find scaling laws for Young's modulus, E, and Ultimate Tensile Strength (UTS) as a function of porosity and composition.
Posted Content
Potential of Neural Networks for Maximum Displacement Predictions in Railway Beams on Frictionally Damped Foundations
TL;DR: This paper aims to demonstrate the potential of Artificial Neural Networks (ANN) to effectively predict the maximum displacements and the critical velocity in railway beams under moving loads.
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.
Book
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?
Book
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.