Knowledge transfer in SVM and neural networks
| Autor: | Rauf Izmailov, Vladimir Vapnik |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Knowledge representation and reasoning business.industry Computer science Applied Mathematics Context (language use) 02 engineering and technology Machine learning computer.software_genre Support vector machine 020901 industrial engineering & automation Inductive transfer Artificial Intelligence Convergence (routing) 0202 electrical engineering electronic engineering information engineering Learning theory 020201 artificial intelligence & image processing Artificial intelligence business Knowledge transfer computer |
| Zdroj: | Annals of Mathematics and Artificial Intelligence. 81:3-19 |
| ISSN: | 1573-7470 1012-2443 |
| DOI: | 10.1007/s10472-017-9538-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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