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ORCID: https://orcid.org/0000-0002-9180-8165, Torres Alegre, Santiago
ORCID: https://orcid.org/0000-0002-9945-105X and Andina de la Fuente, Diego
ORCID: https://orcid.org/0000-0001-7036-2646
(2018).
Tackling business intelligence with bioinspired deep learning.
"Neural computing and applications", v. 29
(n. 4);
pp. 1-8.
ISSN 0941-0643.
https://doi.org/10.1007/s00521-018-3377-5.
| Título: | Tackling business intelligence with bioinspired deep learning |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Neural computing and applications |
| Fecha: | Febrero 2018 |
| ISSN: | 0941-0643 |
| Volumen: | 29 |
| Número: | 4 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Business intelligence, Business data, Bioinspired systems, Metaplasticity, Deep learning, MLP; AMMLP, AMP, Artificial neural network |
| Escuela: | E.T.S.I. Telecomunicación (UPM) |
| Departamento: | Señales, Sistemas y Radiocomunicaciones |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
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To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.
| ID de Registro: | 54998 |
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| Identificador DC: | https://oa.upm.es/54998/ |
| Identificador OAI: | oai:oa.upm.es:54998 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/5653827 |
| Identificador DOI: | 10.1007/s00521-018-3377-5 |
| URL Oficial: | https://link.springer.com/article/10.1007%2Fs00521... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 28 May 2019 15:31 |
| Ultima Modificación: | 12 Nov 2025 00:00 |
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