A parallel approach for backpropagation learning of neural networks
Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly describes parallel schemes for a backpropagation algorithm and proposes a distributed...
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| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
1997
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/23892 |
| Aporte de: |
| id |
I19-R120-10915-23892 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Neutral networks parallelised backpropagation partitioning schemes pattern partitioning system architecture Architectures Parallel Neural nets Distributed |
| spellingShingle |
Ciencias Informáticas Neutral networks parallelised backpropagation partitioning schemes pattern partitioning system architecture Architectures Parallel Neural nets Distributed Crespo, María Liz Piccoli, María Fabiana Printista, Alicia Marcela Gallard, Raúl Hector A parallel approach for backpropagation learning of neural networks |
| topic_facet |
Ciencias Informáticas Neutral networks parallelised backpropagation partitioning schemes pattern partitioning system architecture Architectures Parallel Neural nets Distributed |
| description |
Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time.
This work briefly describes parallel schemes for a backpropagation algorithm and proposes a distributed system architecture for developing parallel training with a partition pattern scheme. Under this approach, weight changes are computed concurrently, exchanged between system components and adjusted accordingly until the whole parallel learning process is completed. Some comparative results are also shown. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Crespo, María Liz Piccoli, María Fabiana Printista, Alicia Marcela Gallard, Raúl Hector |
| author_facet |
Crespo, María Liz Piccoli, María Fabiana Printista, Alicia Marcela Gallard, Raúl Hector |
| author_sort |
Crespo, María Liz |
| title |
A parallel approach for backpropagation learning of neural networks |
| title_short |
A parallel approach for backpropagation learning of neural networks |
| title_full |
A parallel approach for backpropagation learning of neural networks |
| title_fullStr |
A parallel approach for backpropagation learning of neural networks |
| title_full_unstemmed |
A parallel approach for backpropagation learning of neural networks |
| title_sort |
parallel approach for backpropagation learning of neural networks |
| publishDate |
1997 |
| url |
http://sedici.unlp.edu.ar/handle/10915/23892 |
| work_keys_str_mv |
AT crespomarializ aparallelapproachforbackpropagationlearningofneuralnetworks AT piccolimariafabiana aparallelapproachforbackpropagationlearningofneuralnetworks AT printistaaliciamarcela aparallelapproachforbackpropagationlearningofneuralnetworks AT gallardraulhector aparallelapproachforbackpropagationlearningofneuralnetworks AT crespomarializ parallelapproachforbackpropagationlearningofneuralnetworks AT piccolimariafabiana parallelapproachforbackpropagationlearningofneuralnetworks AT printistaaliciamarcela parallelapproachforbackpropagationlearningofneuralnetworks AT gallardraulhector parallelapproachforbackpropagationlearningofneuralnetworks |
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Repositorios |
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1764820466377162752 |