Skip to main navigation
Skip to search
Skip to main content
Princeton University Home
Help & FAQ
Link opens in a new tab
Search content at Princeton University
Home
Profiles
Research units
Facilities
Projects
Research output
Press/Media
Algebraic projection analysis for back-propagation learning
S. Y. Kung
, J. N. Hwang
Electrical and Computer Engineering
Center for Statistics & Machine Learning
Research output
:
Contribution to journal
›
Conference article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Algebraic projection analysis for back-propagation learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Hidden Unit
100%
Backpropagation Learning
100%
Projection Analysis
100%
Learning Rate
66%
Analytical Solution
33%
Convergence Rate
33%
Backpropagation
33%
Postsynaptic Density Protein 95 (PSD-95)
33%
Discrimination Power
33%
Fast Learning
33%
Optimal number
33%
Priori Estimates
33%
Iterative Computation
33%
Hidden Neurons
33%
Unit Size
33%
Optimal Learning Rate
33%
Engineering
Backpropagation Learning
100%
Simulation Result
50%
Backpropagation
50%
Synaptic Weight
50%
Hidden Neuron
50%
Mathematics
Hidden Unit
100%
Hidden Neuron
33%
Biochemistry, Genetics and Molecular Biology
Back Propagation
100%
Solution and Solubility
33%
Chemical Engineering
Backpropagation
100%
Computer Science
Discrimination Capability
33%