Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.provenance | CONICET | - |
| dc.creator | Sánchez Gutiérrez, Máximo | - |
| dc.creator | Albornoz, Enrique Marcelo | - |
| dc.creator | Rufiner, Hugo Leonardo | - |
| dc.creator | Close, John Goddard | - |
| dc.date | 2018-06-01T19:52:10Z | - |
| dc.date | 2018-06-01T19:52:10Z | - |
| dc.date | 2017-08 | - |
| dc.date | 2018-05-31T18:18:57Z | - |
| dc.date.accessioned | 2019-04-29T15:53:20Z | - |
| dc.date.available | 2019-04-29T15:53:20Z | - |
| dc.date.issued | 2017-08 | - |
| dc.identifier | Sánchez Gutiérrez, Máximo; Albornoz, Enrique Marcelo; Rufiner, Hugo Leonardo; Close, John Goddard; Post-training discriminative pruning for RBMs; Springer; Soft Computing; 8-2017 | - |
| dc.identifier | 1432-7643 | - |
| dc.identifier | http://hdl.handle.net/11336/47032 | - |
| dc.identifier | 1433-7479 | - |
| dc.identifier | CONICET Digital | - |
| dc.identifier | CONICET | - |
| dc.identifier.uri | http://rodna.bn.gov.ar:8080/jspui/handle/bnmm/304700 | - |
| dc.description | One of the major challenges in the area of artificial neural networks is the identification of a suitable architecture for a specific problem. Choosing an unsuitable topology can exponentially increase the training cost, and even hinder network convergence. On the other hand, recent research indicates that larger or deeper nets can map the problem features into a more appropriate space, and thereby improve the classification process, thus leading to an apparent dichotomy. In this regard, it is interesting to inquire whether independent measures, such as mutual information, could provide a clue to finding the most discriminative neurons in a network. In the present work we explore this question in the context of Restricted Boltzmann Machines, by employing different measures to realize post-training pruning. The neurons which are determined by each measure to be the most discriminative, are combined and a classifier is applied to the ensuing network to determine its usefulness. We find that two measures in particular seem to be good indicators of the most discriminative neurons, producing savings of generally more than 50% of the neurons, while maintaining an acceptable error rate. Further, it is borne out that starting with a larger network architecture and then pruning is more advantageous than using a smaller network to begin with. Finally, a quantitative index is introduced which can provide information on choosing a suitable pruned network. | - |
| dc.description | Fil: Sánchez Gutiérrez, Máximo. Universidad Autónoma Metropolitana; México | - |
| dc.description | Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina | - |
| dc.description | Fil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; Argentina | - |
| dc.description | Fil: Close, John Goddard. Universidad Autónoma Metropolitana; México | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | Springer | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/10.1007/s00500-017-2784-3 | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s00500-017-2784-3 | - |
| dc.rights | info:eu-repo/semantics/openAccess | - |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | - |
| dc.source | reponame:CONICET Digital (CONICET) | - |
| dc.source | instname:Consejo Nacional de Investigaciones Científicas y Técnicas | - |
| dc.source | instacron:CONICET | - |
| dc.source.uri | http://hdl.handle.net/11336/47032 | - |
| dc.subject | RESTRICTED BOLTZMANN MACHINES | - |
| dc.subject | DISCRIMINATIVE INFORMATION | - |
| dc.subject | PRUNING | - |
| dc.subject | EMOTION CLASSIFICATION | - |
| dc.subject | PHONEME CLASSIFICATION | - |
| dc.subject | Ciencias de la Computación | - |
| dc.subject | Ciencias de la Computación e Información | - |
| dc.subject | CIENCIAS NATURALES Y EXACTAS | - |
| dc.title | Post-training discriminative pruning for RBMs | - |
| dc.type | info:eu-repo/semantics/article | - |
| dc.type | info:eu-repo/semantics/publishedVersion | - |
| dc.type | info:ar-repo/semantics/articulo | - |
| Aparece en las colecciones: | CONICET | |
Ficheros en este ítem:
No hay ficheros asociados a este ítem.