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Knowledge transfer in SVM and neural networks
Authors:Vladimir Vapnik  Rauf Izmailov
Affiliation:1.Columbia University,New York,USA;2.AI Research Lab,Facebook,New York,USA;3.Vencore Labs,Basking Ridge,USA
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.
Keywords:
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