Role of the training algorithm in model selection on neural networks
El rol del algoritmo de entrenamiento en la selección de modelos de redes neuronales
| dc.creator | Sánchez, Paola | |
| dc.creator | Velásquez, Juan | |
| dc.date | 2011-06-30 | |
| dc.date.accessioned | 2023-08-28T15:13:06Z | |
| dc.date.available | 2023-08-28T15:13:06Z | |
| dc.identifier | https://revistas.udca.edu.co/index.php/ruadc/article/view/767 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8442765 | |
| dc.description | The Neural net?s fit ability is often affected by the network configuration, particularly the number of hidden neurons and input variables. As the size of these parameters increases, the learning also increases, then the fit of network is better. Theoretically, if parameters are increasing regularly, the error should be reduced systematically, provided that the models are nested for each step of the process. In this work, we validated the hypothesis that the addition of hidden neurons in nested models lead to systematic reductions in error, regardless of the learning algorithm used; to illustrate the discussion we used the number of airline passengers and Sunspots in Box &Jenkins, and RProp and Delta Rule as learning methods. Experimental evidence shows that the evaluated training methods show different behaviors as those theoretically expected, it means, not fulfilling the assumption of error reduction. | en-US |
| dc.description | La capacidad de ajuste de una red neuronal se ve a menudo afectada por la configuración usada, en especial, en relación al número de neuronas ocultas y de variables de entrada, toda vez que, a medida que el número de parámetros del modelo aumenta, se favorece el aprendizaje de la red y, por tanto, el ajuste es mejor. Teóricamente, un proceso constructivo de adición de parámetros debería conducir a reducciones sistemáticas en el error, siempre y cuando, los modelos sean anidados en cada paso del proceso. En este trabajo, se valida la hipótesis que la adición de neuronas ocultas en modelos anidados debe conducir a reducciones en el error, sin importar el algoritmo de entrenamiento usado; para ejemplificar la discusión, se usaron la serie de pasajeros en líneas aéreas y de manchas solares de Box &Jenkins y los métodos de entrenamiento de Regla Delta y RProp. La evidencia experimental demuestra que los métodos de entrenamiento evaluados exhiben comportamientos diferentes a los teóricamente esperados, incumpliendo el supuesto de reducción del error. | es-ES |
| dc.format | application/pdf | |
| dc.format | text/html | |
| dc.language | spa | |
| dc.publisher | Universidad de Ciencias Aplicadas y Ambientales U.D.C.A | es-ES |
| dc.relation | https://revistas.udca.edu.co/index.php/ruadc/article/view/767/839 | |
| dc.relation | https://revistas.udca.edu.co/index.php/ruadc/article/view/767/840 | |
| dc.relation | /*ref*/ADYA, M.; COLLOPY, F. 1998. How effective are neural networks at forecasting; prediction? A review; evaluation. J. Forecasting (USA). 17:481-495. | |
| dc.relation | /*ref*/ANASTASIADIS, A.D.; MAGOULAS, G.D.; VRAHATIS, M.N. 2003. An Efficient Improvement of the Rprop Algorithm. Proceedings of the First International Workshop on Artificial Neural Networks in Pattern Recognition. University of Florence (ITALY). p.197- 201. | |
| dc.relation | /*ref*/COTTRELL, M.; GIRARD, B.; GIRARD, Y.; MANGEAS, M.; MULLER, C. 1995. Neural modeling for time series: a statistical stepwise method for weight elimination. IEEE Transactions on Neural Networks (USA). 6(6):1355-1364. | |
| dc.relation | /*ref*/CRONE, S.; KOURENTZES, N. 2009. Input-variable Specification for Neural Networks - An Analysis of Forecasting low and high Time Series Frequency. Proceedings of the International Joint Conference on Neural Networks, IJCNN?09 (USA). p.619-626. | |
| dc.relation | /*ref*/FAHLMAN, S. 1989. Faster-learning variations of backpropagation: An empirical study. En: Touretzky, D., Hinton, G.; Sejnowski, T. (eds) Proceedings of the 1988 Connectionist Models Summer School (USA). p.38-51 | |
| dc.relation | /*ref*/FARAWAY, J.; CHATFIELD, C. 1998. Time series forecasting with neural networks: a comparative study using the airline data. Appl. Statist. (USA). 47:231- 250. | |
| dc.relation | /*ref*/GHIASSI, M.; SAIDANE, H.; ZIMBRA, D.K. 2005. A dynamic neural network model for forecasting time series events. International J. Forecasting (USA). 21:341-362. | |
| dc.relation | /*ref*/HAGAN, M.T.; DEMUTH, H.B.; BEALE, M.H. 1996. Neural Network Design. Ed. PWS Publishing,Boston: MA(USA). | |
| dc.relation | /*ref*/HAMILTON, J.D. 1994. Time Series Analysis. Princeton, New Jersey: Princeton University Press (USA). 820p. | |
| dc.relation | /*ref*/HORNIK, K.; STINCHICOMBE, M.; WHITE, H. 1989. Multilayer Feedforward Networks are Universal Approximators. Neural Networks (USA). 2(5):359- 366. | |
| dc.relation | /*ref*/MURATA, N.; YOSHIZAWA, S.; AMARI, S. 1994. Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks (USA). 5:865- 872. | |
| dc.relation | /*ref*/QI, M.; ZHANG, P.G. 2001. An investigation of model selection criteria for neural network time series forecasting. European J. Operational Research (NORWAY). 132:666-680. | |
| dc.relation | /*ref*/TANG, Z.; KOEHLER, J.G. 1994. Deterministic global optimal FNN training algorithms. Neural Networks (USA). 7:1405-1412. | |
| dc.relation | /*ref*/VELÁSQUEZ, J.D.; DYNER, I.; SOUZA, R.C. 2008. Modelado del precio de la electricidad en brasil usando una red neuronal autorregresiva. Ingeniare. Rev. Chilena Ingeniería. 16(3):394-403. | |
| dc.relation | /*ref*/ZHANG, P.G.; PATUWO, B.E.; HU, M.Y. 1998. Forecasting with artificial neural networks: the state of the art. International J. Forecasting (USA). 14(1):35-62. | |
| dc.relation | /*ref*/ZHANG, G.P.; PATUWO, B.E.; HU, M.Y. 2001. A simulation study of artificial neural networks for non linear time-series forecasting. Computers & Operations Research (USA). 28(4):381-396. | |
| dc.source | Revista U.D.C.A Actualidad & Divulgación Científica; Vol. 14 No. 1 (2011): Revista U.D.C.A Actualidad & Divulgación Científica. Enero-Junio; 149-156 | en-US |
| dc.source | Revista U.D.C.A Actualidad & Divulgación Científica; Vol. 14 Núm. 1 (2011): Revista U.D.C.A Actualidad & Divulgación Científica. Enero-Junio; 149-156 | es-ES |
| dc.source | Revista U.D.C.A Actualidad & Divulgación Científica; v. 14 n. 1 (2011): Revista U.D.C.A Actualidad & Divulgación Científica. Enero-Junio; 149-156 | pt-BR |
| dc.source | 2619-2551 | |
| dc.source | 0123-4226 | |
| dc.source | 10.31910/rudca.v14.n1.2011 | |
| dc.subject | Redes Neuronales | es-ES |
| dc.subject | Algoritmo de Entrenamiento | es-ES |
| dc.subject | Artificial neural networks | en-US |
| dc.subject | Training algorithm | en-US |
| dc.title | Role of the training algorithm in model selection on neural networks | en-US |
| dc.title | El rol del algoritmo de entrenamiento en la selección de modelos de redes neuronales | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion |