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2003, Cambridge monographs on applied and computational mathematics, ISBN 9780521633383, Volume 12, x, 259
In many areas of mathematics, science and engineering, from computer graphics to inverse methods to signal processing, it is necessary to estimate parameters,... 
Radial basis functions
Book
IEEE Transactions on Power Systems, ISSN 0885-8950, 11/2012, Volume 27, Issue 4, pp. 1788 - 1796
Journal Article
2015, CBMS-NSF regional conference series in applied mathematics, ISBN 9781611974027, Volume 87, x, 221
Book
Journal Article
Journal of Computational Physics, ISSN 0021-9991, 06/2018, Volume 363, p. 87
We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as... 
Radial basis function | Algorithms | Parameters | Basis functions | Stochastic models | Computational physics | Computational efficiency | Parameter identification
Journal Article
Applied Mathematical Modelling, ISSN 0307-904X, 11/2017, Volume 51, pp. 728 - 743
Approximation of scattered data is often a task in many engineering problems. The radial basis function (RBF) approximation is appropriate for large scattered... 
Radial basis function | Approximation | Lagrange multipliers | RBF | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | ENGINEERING, MULTIDISCIPLINARY
Journal Article
SIAM Journal on Scientific Computing, ISSN 1064-8275, 2012, Volume 34, Issue 2, pp. A737 - A762
We provide a new way to compute and evaluate Gaussian radial basis function interpolants in a stable way with a special focus on small values of the shape... 
Eigenfunction expansion | Mercer's theorem | Gaussian kernel | Radial basis functions | Stable evaluation | QR decomposition | stable evaluation | MATHEMATICS, APPLIED | APPROXIMATION | eigenfunction expansion | EQUATIONS | CONVERGENCE | radial basis functions | LIMIT
Journal Article
Advances in Engineering Software, ISSN 0965-9978, 09/2018, Volume 123, pp. 117 - 129
Journal Article
IEEE Transactions on Fuzzy Systems, ISSN 1063-6706, 04/2015, Volume 23, Issue 2, pp. 457 - 473
Journal Article
Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, 11/2019, Volume 534, p. 122168
The present work brings two applications of the Lambert–Tsallis function in radial basis function networks (RBFN). Initially, an RBFN is used to discriminate... 
Quantum relative disentropy | Lambert–Tsallis [formula omitted] function | Radial basis function network
Journal Article
2009, Publications on geodesy / Netherlands Geodetic Commission, ISBN 9061323150, Volume no. 72., xxii, 191
Book
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 3/2019, pp. 1 - 1
Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning. The most popular approach for training RBF networks has... 
Training | Supervised learning | K-Means | Training data | Radial basis function networks | Loss measurement | Kernel | Standards
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 2011, Volume 24, Issue 3, pp. 378 - 385
Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external... 
K-means clustering algorithm | Shanghai Stock Exchange Index | Artificial fish swarm algorithm | Data mining | Radial basis function neural network | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
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