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Passive-Aggressive online learning with nonlinear embeddings

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Passive-Aggressive online learning with nonlinear embeddings

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dc.contributor.author Jorge-Cano, Javier es_ES
dc.contributor.author Paredes Palacios, Roberto es_ES
dc.date.accessioned 2019-09-14T20:01:36Z
dc.date.available 2019-09-14T20:01:36Z
dc.date.issued 2018 es_ES
dc.identifier.issn 0031-3203 es_ES
dc.identifier.uri http://hdl.handle.net/10251/125683
dc.description.abstract [EN] Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time. In these scenarios, online learning is able to learn a model from data that comes continuously. The adaptability, efficiency and scalability of online learning techniques have been gaining interest last years with the increasing amount of data generated every day. In this paper, we propose a novel binary classification approach based on nonlinear mapping functions under an online learning framework. The non-convex optimization problem that arises is split into three different convex problems that are solved by means of Passive-Aggressive Online Learning. We evaluate both the adaptability and generalization of our model through several experiments comparing with the state of the art techniques. We improve significantly the results in several datasets widely used previously by the online learning community. (C) 2018 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This work was developed in the framework of the PROM-ETEOII/2014/030 research project "Adaptive learning and multi modality in machine translation and text transcription", funded by the Generalitat Valenciana. The work of the first author is financed by Grant FPU14/03981, from the Spanish Ministry of Education, Culture and Sport. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Pattern Recognition es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Online learning es_ES
dc.subject Nonlinear functions es_ES
dc.subject Passive-Aggressive es_ES
dc.subject Binary classification es_ES
dc.subject Nonlinear embedding es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Passive-Aggressive online learning with nonlinear embeddings es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.patcog.2018.01.019 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2014%2F030/ES/ Adaptive learning and multimodality in machine translation and text transcription/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU2014-03981/ES/FPU2014-03981/ es_ES
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 Jorge-Cano, J.; Paredes Palacios, R. (2018). Passive-Aggressive online learning with nonlinear embeddings. Pattern Recognition. 79:162-171. https://doi.org/10.1016/j.patcog.2018.01.019 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1016/j.patcog.2018.01.019 es_ES
dc.description.upvformatpinicio 162 es_ES
dc.description.upvformatpfin 171 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 79 es_ES
dc.relation.pasarela S\362440 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Ministerio de Educación es_ES


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