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
Title:
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A knowledge growth and consolidation framework for lifelong machine learning systems
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Author:
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Martínez-Plumed, Fernando
Ferri Ramírez, César
Hernández Orallo, José
Ramírez Quintana, María José
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UPV Unit:
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Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
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Issued date:
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Abstract:
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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 ...[+]
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 view of machine learning goes beyond most of state-of-the-art machine learning techniques that learn throw-away models. In this paper we present a long-life knowledge acquisition, evaluation and consolidation framework that is designed to work with any rule-based machine learning or inductive inference engine and integrate it into a long-life learner. In order to do that we work over the graph of working memory rules and introduce several topological metrics over it from which we derive an oblivion criterion to drop useless rules from working memory and a consolidation process to promote the rules to the knowledge base. We evaluate the framework on a series of tasks in a chess rule learning domain.
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Subjects:
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Lifelong machine learning
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Oblivion criterion
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Knowledge topology and acquisition
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Declarative learning
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Copyrigths:
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Reserva de todos los derechos
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ISBN:
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978-1-4799-7415-3
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Source:
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2014 13th International Conference on Machine Learning and Applications.
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DOI:
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10.1109/ICMLA.2014.23
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Publisher:
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IEEE
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Publisher version:
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http://dx.doi.org/10.1109/ICMLA.2014.23
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Conference name:
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13th International Conference on Machine Learning and Applications (ICMLA 2014)
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Conference place:
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Detroit, USA
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Conference date:
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December 3-6, 2014
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Description:
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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.
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Type:
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Comunicación en congreso
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