Neural network learning for analog VLSI implementations of support vector machines: a survey
| Autor: | A. Boni, Davide Anguita |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2003 |
| Předmět: |
Hyperparameter
Computer science business.industry Cognitive Neuroscience Model selection SVM learning Neural network learning Recurrent networks Quadratic programming Machine learning computer.software_genre Computer Science Applications Nonlinear programming Support vector machine Recurrent neural network Artificial Intelligence Analog VLSI Vlsi implementations Artificial intelligence SVM learning Recurrent networks Analog VLSI Quadratic programming business computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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