Extreme learning machine adapted to noise based on optimization algorithms

Vásquez, A.; Mora, Marco; Salazar, E.; Gelvez, E.

Mostrar el registro sencillo de la publicación

dc.contributor.author	Vásquez, A.
dc.contributor.author	Mora, Marco
dc.contributor.author	Salazar, E.
dc.contributor.author	Gelvez, E.
dc.date.accessioned	2020-12-29T12:37:07Z
dc.date.available	2020-12-29T12:37:07Z
dc.date.issued	2020
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3388
dc.description.abstract	The extreme learning machine for neural networks of feedforward of a single hidden layer randomly assigns the weights of entry and analytically determines the weights the output by means the Moore-Penrose inverse, this algorithm tends to provide an extremely fast learning speed preserving the adjustment levels achieved by classifiers such as multilayer perception and support vector machine. However, the Moore-Penrose inverse loses precision when using data with additive noise in training. That is why in this paper a method to robustness of extreme learning machine to additive noise proposed. The method consists in computing the weights of the output layer using non-linear optimization algorithms without restrictions. Tests are performed with the gradient descent optimization algorithm and with the Levenberg-Marquardt algorithm. From the implementation it is observed that through the use of these algorithms, smaller errors are achieved than those obtained with the Moore-Penrose inverse.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Journal of Physics: Conference Series, 1514, 012006	es_CL
dc.title	Extreme learning machine adapted to noise based on optimization algorithms	es_CL
dc.type	Article	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.doi	doi.org/10.1088/1742-6596/1514/1/012006	es_CL