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Learning with con gurable operators and RL-based heuristics

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Learning with con gurable operators and RL-based heuristics

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dc.contributor.author Martínez Plumed, Fernando es_ES
dc.contributor.author Ferri Ramírez, César es_ES
dc.contributor.author Hernández Orallo, José es_ES
dc.contributor.author Ramírez Quintana, María José es_ES
dc.date.accessioned 2014-05-08T12:06:02Z
dc.date.issued 2013-10
dc.identifier.isbn 978-3-642-37381-7
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/37322
dc.description.abstract In this paper, we push forward the idea of machine learning systems for which the operators can be modi ed and netuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators a ect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is de ned as a choice of operator and rule. As a result, the architecture can be seen as a `system for writing machine learning systems' or to explore new operators. es_ES
dc.description.sponsorship This work was supported by the MEC projects CONSOLIDER-INGENIO 26706 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Econom´ıa y Competitividad in Spain. Also, F. Mart´ınez-Plumed is supported by FPI-ME grant BES-2011-045099
dc.format.extent 16 es_ES
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation MICINN/CONSOLIDER-INGENIO 26706 es_ES
dc.relation GV/PROMETEO/2008/051 es_ES
dc.relation MINECO-MICINN/TIN 2010-21062-C02-02
dc.relation MICINN/BES-2011-045099
dc.relation.ispartof New Frontiers in Mining Complex Patterns es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;7765
dc.rights Reserva de todos los derechos es_ES
dc.subject Machine learning operators es_ES
dc.subject Complex data es_ES
dc.subject Heuristics es_ES
dc.subject Inducting programming es_ES
dc.subject Reinforcement learning es_ES
dc.subject Erlang es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Learning with con gurable operators and RL-based heuristics es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-642-37382-4_1
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Martínez Plumed, F.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2013). Learning with con gurable operators and RL-based heuristics. En New Frontiers in Mining Complex Patterns. Springer Verlag (Germany). 7765:1-16. doi:10.1007/978-3-642-37382-4_1 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename First International Workshop, NFMCP 2012 Held in Conjunction with ECML/PKDD 2012 es_ES
dc.relation.conferencedate September 24, 2012 es_ES
dc.relation.conferenceplace Bristol, UK es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/978-3-642-37382-4_1 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 16 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7765 es_ES
dc.relation.senia 238785
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Ministerio de Economía y Competitividad
dc.contributor.funder Generalitat Valenciana
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