Knowledge transfer in SVM and neural networks

Knowledge transfer in SVM and neural networks The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Knowledge transfer in SVM and neural networks

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing Switzerland
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
D.O.I.
10.1007/s10472-017-9538-x
Publisher site
See Article on Publisher Site

Abstract

The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Feb 20, 2017

References

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