Tackling business intelligence with bioinspired deep learning

Fombellida Vetas, Juan, Martín Rubio, Irene 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.

Descripción

Título: Tackling business intelligence with bioinspired deep learning
Autor/es:
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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Resumen

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.

Más información

ID de Registro: 54998
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