Artículo
Representative Datasets: The Perceptron Case
Autor/es | González Díaz, Rocío
Gutiérrez Naranjo, Miguel Ángel Paluzo Hidalgo, Eduardo |
Departamento | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2019 |
Fecha de depósito | 2020-06-17 |
Publicado en |
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Resumen | One of the main drawbacks of the practical use of neural networks is the long time needed in
the training process. Such training process consists in an iterative change of parameters trying to
minimize a loss function. ... One of the main drawbacks of the practical use of neural networks is the long time needed in the training process. Such training process consists in an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labeled points in an n-dimensional space. In this paper, we explore the concept of representative dataset which is smaller than the original dataset and satisfies a nearness condition independent of isometric transformations. The representativeness is measured using persistence diagrams due to its computational efficiency. We also prove that the accuracy of the learning process of a neural network on a representative dataset is comparable with the accuracy on the original dataset when the neural network architecture is a perceptron and the loss function is the mean squared error. These theoretical results accompanied with experimentation open a door to the size reduction of the dataset in order to gain time in the training process of any neural network. |
Cita | González Díaz, R., Gutiérrez Naranjo, M.Á. y Paluzo Hidalgo, E. (2019). Representative Datasets: The Perceptron Case. ArXiv.org, arXiv:1903.08519 |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Representative Datasets.pdf | 1.204Mb | [PDF] | Ver/ | |