Generally, the resolution of a problem by using soft-computing support requires several attempts for setting up a proper neural network. Such attempts consist of designing and training a neural network and can be a relevant effort for the developer. This paper proposes a toolbox that automates several steps for setting up a neural network, and provides high-level abstractions allowing a developer to choose classical network topologies and configure them as desired, as well as design a neural network from a scratch. A valuable aspect of our solution is given by the modularity of the whole design that builds on object-orientation and design patterns.
An Object-Oriented Neural Network Toolbox Based on Design Patterns / Napoli, C; Tramontana, E. - 538:(2015), pp. 388-399. (Intervento presentato al convegno 21st International Conference on Information and Software Technologies, ICIST 2015 tenutosi a Druskininkai; Lithuania) [10.1007/978-3-319-24770-0_34].
An Object-Oriented Neural Network Toolbox Based on Design Patterns
Napoli C
;
2015
Abstract
Generally, the resolution of a problem by using soft-computing support requires several attempts for setting up a proper neural network. Such attempts consist of designing and training a neural network and can be a relevant effort for the developer. This paper proposes a toolbox that automates several steps for setting up a neural network, and provides high-level abstractions allowing a developer to choose classical network topologies and configure them as desired, as well as design a neural network from a scratch. A valuable aspect of our solution is given by the modularity of the whole design that builds on object-orientation and design patterns.File | Dimensione | Formato | |
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Note: https://link.springer.com/chapter/10.1007/978-3-319-24770-0_34
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