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Backpropagation

Known as: Error back-propagation, Backpropogation, Back prop 
Backpropagation, an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks used in conjunction… 
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Papers overview

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2012
2012
Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets
This paper proposes an automatic system for early detection of liver diseases from Computed tomography (CT) images. The general… 
2010
2010
Neural network modeling for Ni(II) removal from aqueous system using shelled Moringa oleifera seed powder as an agricultural waste.
A single-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Ni(II) ions from aqueous… 
Highly Cited
2009
Highly Cited
2009
Future Generation Information Technology, First International Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009. Proceedings
Keynotes.- Computer Science: Where Is the Next Frontier?.- Video Forgery.- Data Analysis, Data Processing, Advanced Computation… 
Highly Cited
2000
Highly Cited
2000
Inversions of radio occultation amplitude data
Radio occultation remote sensing of the Earth's atmosphere consists of satellite‐to‐satellite observations of phase and amplitude… 
Highly Cited
1998
Highly Cited
1998
Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture
Pruning methods for feed-forward artificial neural networks trained by the cascade-correlation learning algorithm are proposed… 
1997
1997
Figure 4. Typical Evolution of a Population of Networks Generated by (a) Mta Algorithm, (b) Cca Algorithm and (c) Ccas Algorithm. (a) (b) 6. Acknowledgments 7. References
[4] F. Dellaert, and J. Vandewalle, " Automatic design of cellular neural networks by means of genetic algorithms: finding a… 
Highly Cited
1995
Highly Cited
1995
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions… 
1995
1995
Comments on "Noise injection into inputs in back propagation learning"
The generalization capacity of neural networks learning from examples is important. Several authors showed experimentally that… 
1990
1990
Multi-layer perceptrons with discrete weights
The feasibility of restricting the weight values in multilayer perceptrons to powers of two or sums of powers of two is studied… 
Highly Cited
1988
Highly Cited
1988
Parallel architectures for artificial neural nets
The authors advocate digital VLSI architectures for implementing a wide variety of artificial neural nets (ANNs). A programmable… 
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