dor_id: 45796

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

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883.#.#.u: https://revistas.unam.mx/catalogo/

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/98/97

100.1.#.a: Wang, Jenq Haur; Wang, Hsin Yang; Chen, Yen Lin; Liu, Chuan Ming

524.#.#.a: Wang, Jenq Haur, et al. (2015). A constructive algorithm for unsupervised learning with incremental neural network. Journal of Applied Research and Technology; Vol. 13 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/45796

245.1.0.a: A constructive algorithm for unsupervised learning with incremental neural network

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2015

264.#.1.c: 2015-04-01

653.#.#.a: Artificial neural network; Unsupervised learning; Text classification

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

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041.#.7.h: eng

520.3.#.a: Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neural network is incrementally constructed by the corresponding subnets with individual instances. First, a subnet maps the relation between inputs and outputs for an observed instance. Then, when combining multiple subnets, the neural network keeps the corresponding abilities to generate the same outputs with the same inputs. This makes the learning process unsupervised and inherent in this framework. In our experiment, Reuters-21578 was used as the dataset to show the effectiveness of the proposed method on text classification. The experimental results showed that our method can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. This framework also validates scalability in terms of the network size, in which the training and testing times both showed a constant trend. This also validates the feasibility of the method for practical uses. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.

773.1.#.t: Journal of Applied Research and Technology; Vol. 13 Núm. 2

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.1016/j.jart.2015.06.017

harvesting_date: 2023-11-08 13:10:00.0

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last_modified: 2024-03-19 14:00:00

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Artículo

A constructive algorithm for unsupervised learning with incremental neural network

Wang, Jenq Haur; Wang, Hsin Yang; Chen, Yen Lin; Liu, Chuan Ming

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Wang, Jenq Haur, et al. (2015). A constructive algorithm for unsupervised learning with incremental neural network. Journal of Applied Research and Technology; Vol. 13 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/45796

Descripción del recurso

Autor(es)
Wang, Jenq Haur; Wang, Hsin Yang; Chen, Yen Lin; Liu, Chuan Ming
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
A constructive algorithm for unsupervised learning with incremental neural network
Fecha
2015-04-01
Resumen
Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neural network is incrementally constructed by the corresponding subnets with individual instances. First, a subnet maps the relation between inputs and outputs for an observed instance. Then, when combining multiple subnets, the neural network keeps the corresponding abilities to generate the same outputs with the same inputs. This makes the learning process unsupervised and inherent in this framework. In our experiment, Reuters-21578 was used as the dataset to show the effectiveness of the proposed method on text classification. The experimental results showed that our method can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. This framework also validates scalability in terms of the network size, in which the training and testing times both showed a constant trend. This also validates the feasibility of the method for practical uses. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.
Tema
Artificial neural network; Unsupervised learning; Text classification
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces