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A knowledge growth and consolidation framework for lifelong machine learning systems

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A knowledge growth and consolidation framework for lifelong machine learning systems

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Martínez-Plumed, F.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2014). A knowledge growth and consolidation framework for lifelong machine learning systems. IEEE. doi:10.1109/ICMLA.2014.23

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/68390

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Title: A knowledge growth and consolidation framework for lifelong machine learning systems
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
A more effective vision of machine learning systems entails tools that are able to improve task after task and to reuse the patterns and knowledge that are acquired previously for future tasks. This incremental, long-life ...[+]
Subjects: Lifelong machine learning , Oblivion criterion , Knowledge topology and acquisition , Declarative learning
Copyrigths: Reserva de todos los derechos
ISBN: 978-1-4799-7415-3
Source:
2014 13th International Conference on Machine Learning and Applications.
DOI: 10.1109/ICMLA.2014.23
Publisher:
IEEE
Publisher version: http://dx.doi.org/10.1109/ICMLA.2014.23
Conference name: 13th International Conference on Machine Learning and Applications (ICMLA 2014)
Conference place: Detroit, USA
Conference date: December 3-6, 2014
Description: 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: Comunicación en congreso

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