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EnglishIn the last few years several kinds of recurrent neural networks (RNNs) have been proposed for solving linear and nonlinear optimization problems. In this paper, we provide a survey of RNNs that can be used to solve both the constrained quadratic optimization problem related to support vector machine (SVM) learning, and the SVM model selection by automatic hyperparameter tuning. The appeal of this approach is the possibility of implementing such networks on analog VLSI systems with relative easiness. We review several proposals appeared so far in the literature and test their behavior when applied to solve a telecommunication application, where a special purpose adaptive hardware is of great interest.
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| Titolo: | Neural network learning for analog VLSI implementations of support vector machines: a survey |
| Autori: | |
| Data di pubblicazione: | 2003 |
| Rivista: | |
| Abstract: | In the last few years several kinds of recurrent neural networks (RNNs) have been proposed for solving linear and nonlinear optimization problems. In this paper, we provide a survey of RNNs that can be used to solve both the constrained quadratic optimization problem related to support vector machine (SVM) learning, and the SVM model selection by automatic hyperparameter tuning. The appeal of this approach is the possibility of implementing such networks on analog VLSI systems with relative easiness. We review several proposals appeared so far in the literature and test their behavior when applied to solve a telecommunication application, where a special purpose adaptive hardware is of great interest. |
| Handle: | http://hdl.handle.net/11567/208710 |
| Appare nelle tipologie: | 01.01 - Articolo su rivista |
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