Difference between revisions of "2013 2013SpecialIssueLearningthePseu"

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* ([[2013_2013SpecialIssueLearningthePseu|Tapson & Van Schaik, 2013]]) ⇒ [[author::J. Tapson]], and [[author::A. Van Schaik]]. ([[year::2013]]). “[https://www.westernsydney.edu.au/__data/assets/pdf_file/0003/783156/Tapson,_van_Schaik_-_2013_-_Learning_the_pseudoinverse_solution_to_network_weights.pdf 2013 Special Issue: Learning the Pseudoinverse Solution to Network Weights].” In: Neural Networks Journal, 45. [http://dx.doi.org/10.1016/j.neunet.2013.02.008 doi:10.1016/j.neunet.2013.02.008]  
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* ([[2013_2013SpecialIssueLearningthePseu|Tapson & Van Schaik, 2013]]) [[author::J. Tapson]], and [[author::A. Van Schaik]]. ([[year::2013]]). “[https://www.westernsydney.edu.au/__data/assets/pdf_file/0003/783156/Tapson,_van_Schaik_-_2013_-_Learning_the_pseudoinverse_solution_to_network_weights.pdf 2013 Special Issue: Learning the Pseudoinverse Solution to Network Weights].” In: Neural Networks Journal, 45. [http://dx.doi.org/10.1016/j.neunet.2013.02.008 doi:10.1016/j.neunet.2013.02.008]  
  
 
<B>Subject Headings:</B> [[Pseudo-Inverse Algorithm]]; [[OPIUM Algorithm]].
 
<B>Subject Headings:</B> [[Pseudo-Inverse Algorithm]]; [[OPIUM Algorithm]].
  
==Notes==
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== Notes ==
  
==Cited By==
 
* http://scholar.google.com/scholar?q=%222013%22+2013+Special+Issue%3A+Learning+the+Pseudoinverse+Solution+to+Network+Weights
 
* http://dl.acm.org/citation.cfm?id=2514178.2514437&preflayout=flat#citedby
 
  
  
==Quotes==
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== Cited By ==
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* [[Google Scholar]]: 71 citations [http://scholar.google.com/scholar?q=%222013%22+2013+Special+Issue%3A+Learning+the+Pseudoinverse+Solution+to+Network+Weights]
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* [[ACM DL]]: 9 citations [http://dl.acm.org/citation.cfm?id=2514178.2514437&preflayout=flat#citedby]
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* [[ScienceDirect Elsevier]]: 46 citations [https://www.sciencedirect.com/science/article/pii/S089360801300049X]
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* [[Semantic Scholar]]: 52 citations [https://www.semanticscholar.org/paper/Learning-the-pseudoinverse-solution-to-network-Tapson-Schaik/21250f1210a401e25a5d93dbaea504ce0eb262eb]
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== Quotes ==
  
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=== Author Keywords ===
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* [[Moore-Penrose Pseudoinverse]], [[Neural Engineering]], [[Extreme Learning Machine]], [[Biological Plausibility]].
  
===Abstract===
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=== Abstract ===
  
 
The last [[decade]] has seen the [[parallel emergence]] in [[computational neuroscience]] and [[machine learning]] of [[neural network]] structures which spread the [[input signal]] [[randomly]] to a [[higher dimensional space]]; perform a [[nonlinear activation]]; and then solve for a [[regression]] or [[classification]] [[output]] by means of a [[mathematical pseudoinverse operation]]. </s>
 
The last [[decade]] has seen the [[parallel emergence]] in [[computational neuroscience]] and [[machine learning]] of [[neural network]] structures which spread the [[input signal]] [[randomly]] to a [[higher dimensional space]]; perform a [[nonlinear activation]]; and then solve for a [[regression]] or [[classification]] [[output]] by means of a [[mathematical pseudoinverse operation]]. </s>
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The [[method]] is significantly more [[memory-efficient]] than the [[conventional computation]] of [[pseudoinverse]]s by [[singular value decomposition]]. </s>
 
The [[method]] is significantly more [[memory-efficient]] than the [[conventional computation]] of [[pseudoinverse]]s by [[singular value decomposition]]. </s>
  
==References==
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=== Copyright ===
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2013 Elsevier Ltd. All rights reserved.
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== References ==
 
{{#ifanon:|
 
{{#ifanon:|
 
* 1. Omri Barak, Mattia Rigotti, A Simple Derivation of a Bound on the Perceptron Margin Using Singular Value Decomposition, Neural Computation, v.23 n.8, p.1935-1943, August 2011 [https://dx.doi.org/10.1162/NECO_a_00152 doi:10.1162/NECO_a_00152]
 
* 1. Omri Barak, Mattia Rigotti, A Simple Derivation of a Bound on the Perceptron Margin Using Singular Value Decomposition, Neural Computation, v.23 n.8, p.1935-1943, August 2011 [https://dx.doi.org/10.1162/NECO_a_00152 doi:10.1162/NECO_a_00152]

Revision as of 13:29, 13 July 2019

Subject Headings: Pseudo-Inverse Algorithm; OPIUM Algorithm.

Notes

Cited By

Quotes

Author Keywords

Abstract

The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional space; perform a nonlinear activation; and then solve for a regression or classification output by means of a mathematical pseudoinverse operation. In the field of neuromorphic engineering, these methods are increasingly popular for synthesizing biologically plausible neural networks, but the "learning method"-computation of the pseudoinverse by singular value decomposition-is problematic both for biological plausibility and because it is not an online or an adaptive method. We present an online or incremental method of computing the pseudoinverse precisely, which we argue is biologically plausible as a learning method, and which can be made adaptable for non-stationary data streams. The method is significantly more memory-efficient than the conventional computation of pseudoinverses by singular value decomposition.

Copyright

2013 Elsevier Ltd. All rights reserved.

References

;

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2013 2013SpecialIssueLearningthePseuJ. Tapson
A. Van Schaik
2013 Special Issue: Learning the Pseudoinverse Solution to Network Weights10.1016/j.neunet.2013.02.0082013
AuthorJ. Tapson + and A. Van Schaik +
doi10.1016/j.neunet.2013.02.008 +
title2013 Special Issue: Learning the Pseudoinverse Solution to Network Weights +
year2013 +