Koniocortex-Like Network Application to Business Intelligence

Fombedilla, J. and Andina de la Fuente, Diego ORCID: https://orcid.org/0000-0001-7036-2646 (2018). Koniocortex-Like Network Application to Business Intelligence. In: "World Automation Congress (WAC 2018)", 03/06/2018 - 06/06/2018, Stevenson, Washington, USA. pp. 1-6. https://doi.org/10.23919/WAC.2018.8430298.


Title: Koniocortex-Like Network Application to Business Intelligence
Item Type: Presentation at Congress or Conference (Article)
Event Title: World Automation Congress (WAC 2018)
Event Dates: 03/06/2018 - 06/06/2018
Event Location: Stevenson, Washington, USA
Title of Book: World Automation Congress (WAC 2018)
Date: 2018
Freetext Keywords: Metaplasticity; Koniocortex; KLN; Business Intelligence; ACAD; Feature Extraction; Competition
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Koniocortex-Like Network model is a Bio-Inspired Neural Network structure that tries to replicate the architecture and properties of the biological koniocortex section of the brain. The structure is composed by different kinds of artificial neurons that interplay between them to create a competitive model that can be used to classify patterns. The classification performance obtained is based on different properties like lateral inhibition, metaplasticity and intrinsic plasticity, that allows a natural evolution of the network until obtaining the desired results. This kind of network has been applied to synthetic and real data showing big potential, now the network capabilities are tested using other state-of-the-art real data application: the classification of credit data from the Australian Credit Approval Database.

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Item ID: 55154
DC Identifier: https://oa.upm.es/55154/
OAI Identifier: oai:oa.upm.es:55154
DOI: 10.23919/WAC.2018.8430298
Official URL: https://ieeexplore.ieee.org/document/8430298
Deposited by: Memoria Investigacion
Deposited on: 27 May 2019 17:35
Last Modified: 27 May 2019 17:35
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