Mostrar el registro sencillo del ítem
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 |