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dc.contributor.authorMarrón Vida, Diego
dc.contributor.authorRead, Jesse
dc.contributor.authorBifet Figuerol, Albert Carles
dc.contributor.authorAbdessalem, Talel
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorHerrero Zaragoza, José Ramón
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2016-12-07T13:41:04Z
dc.date.issued2016
dc.identifier.citationMarron, D., Read, J., Bifet, A., Abdessalem, T., Ayguade, E., Herrero, J. Echo state hoeffding tree learning. A: Asian Conference on Machine Learning. "JMLR Workshop and Conference Proceedings: Volume 63: proceedings of the 8th Asian Conference on Machine Learning: the University of Waikato, Hamilton: November 16-18 2016". Hamilton: Microtome Publishing, 2016, p. 382-397.
dc.identifier.isbn1938-7228
dc.identifier.urihttp://hdl.handle.net/2117/97868
dc.description.abstractNowadays, real-time classi cation of Big Data streams is becoming essential in a variety of application domains. While decision trees are powerful and easy{to{deploy approaches for accurate and fast learning from data streams, they are unable to capture the strong temporal dependences typically present in the input data. Recurrent Neural Networks are an alternative solution that include an internal memory to capture these temporal dependences; however their training is computationally very expensive, with slow convergence and not easy{to{deploy (large number of hyper-parameters). Reservoir Computing was proposed to reduce the computation requirements of the training phase but still include a feed-forward layer which requires a large number of parameters to tune. In this work we propose a novel architecture for real-time classification based on the combination of a Reservoir and a decision tree. This combination makes classification fast, reduces the number of hyper-parameters and keeps the good temporal properties of recurrent neural networks. The capabilities of the proposed architecture to learn some typical string-based functions with strong temporal dependences are evaluated in the paper. The paper shows how the new architecture is able to incrementally learn these functions in real-time with fast adaptation to unknown sequences and analyzes the influence of the reduced number of hyper-parameters in the behaviour of the proposed solution.
dc.format.extent16 p.
dc.language.isoeng
dc.publisherMicrotome Publishing
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.otherReal-time classification
dc.subject.otherBig data streams
dc.subject.otherEcho State Network
dc.subject.otherHoeffding Tree
dc.subject.otherincremental learning
dc.subject.otherTemporal dependencies
dc.titleEcho state hoeffding tree learning
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.jmlr.org/proceedings/papers/v63/
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac19309895
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/SEV-2015-0493
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
dc.date.lift10000-01-01
local.citation.authorMarron, D.; Read, J.; Bifet, A.; Abdessalem, T.; Ayguade, E.; Herrero, J.
local.citation.contributorAsian Conference on Machine Learning
local.citation.pubplaceHamilton
local.citation.publicationNameJMLR Workshop and Conference Proceedings: Volume 63: proceedings of the 8th Asian Conference on Machine Learning: the University of Waikato, Hamilton: November 16-18 2016
local.citation.startingPage382
local.citation.endingPage397


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