Skip to main navigation
Skip to search
Skip to main content
National Yang Ming Chiao Tung University Academic Hub Home
English
中文
Home
Profiles
Research units
Research output
Projects
Prizes
Activities
Equipment
Impacts
Search by expertise, name or affiliation
Training time of grid Gaussian networks increases at power order of input dimension
M. H. Lin
*
,
Fu-Chuang Chen
*
Corresponding author for this work
Department of Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Training time of grid Gaussian networks increases at power order of input dimension'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
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%