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Training time of grid Gaussian networks increases at power order of input dimension
M. H. Lin
*
, Fu-Chuang Chen
*
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電機工程學系
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Computer Science
Computer Hardware
100%
Computer Simulation
100%
Input Dimension
100%
Learning Rate
100%
Nonlinear Mapping
100%
Parallel Computer
100%
Radial Basis Function
100%
Training Process
100%
Keyphrases
Formal Analysis
50%
Gaussian Networks
100%
Gradient Rule
100%
Gradient-based Learning
50%
Learning Rate
50%
Learning Rule
50%
N-dimensional
50%
Nonlinear Mapping
50%
Parallel Computing
50%
Radial Basis Function Neural Network (RBFNN)
50%
Training Process
50%
Training Time
100%
Mathematics
Approximates
50%
Basis Function
50%
Cube
50%
Formal Analysis
50%
Gaussian Distribution
100%
Learning Rule
50%
Nonlinear Mapping
50%
Training Process
50%
Engineering
Gaussians
100%
Learning Rule
50%
Parallel Computer
50%
Radial Basis Function Network
50%
Chemical Engineering
Radial Basis Function Network
100%