Regularization methods vs large training sets
Por:
Vega J., Carrillo-Calvet H., Jiménez Andrade J.L.
Publicada:
1 ene 2019
Categoría:
Multidisciplinary
Resumen:
Digital pulse shape analysis (DPSA) is becoming an essential tool to extract relevant information from waveforms arising from different source. For instance, in the nuclear particle detector field, digital techniques are competing very favorable against the traditional analog way to extract the information contained in the pulses coming from particle detectors. Nevertheless, the extraction of the information contained in these digitized pulses requires powerful methods. One can visualize this extracting procedure as a pattern recognition problem. To approach this problem one can use different alternatives. One very popular alternative is to use an artificial neural network (ANN) as a pattern identifier. When using an ANN, it is common to introduce a regularization method in order to get rid or at least to reduce the effects of overfitting and overtraining. In addition, another option that helps to solve these problems is to use a large training dataset to train the ANN. In this paper, we make an intercomparison of the advantage of regularization methods vs large training datasets when used as methods to reduce the overtraining and overfitting effects when training an ANN. © Copyright owned by the author(s) under the terms of the Creative Commons.
Filiaciones:
Vega J.:
Departamento de Aceleradores, Instituto Nacional de Investigaciones Nucleares, P.O. Box 18-1027, Ciudad de México, 11801, Mexico
Carrillo-Calvet H.:
Laboratorio de Dinámica no Lineal, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, 04510, Mexico
Jiménez Andrade J.L.:
Laboratorio de Dinámica no Lineal, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, 04510, Mexico
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