Show simple item record

dc.contributor.author Grinblat, Guillermo Luis
dc.contributor.author Uzal, Lucas César
dc.contributor.author Ceccatto, Hermenegildo A.
dc.contributor.author Granitto, Pablo Miguel
dc.date.available 2017-04-12T20:21:29Z
dc.date.issued 2011-01
dc.identifier.citation Grinblat, Guillermo Luis; Uzal, Lucas César; Ceccatto, Hermenegildo A.; Granitto, Pablo Miguel; Solving nonstationary classification problems with coupled support vector machines; Institute Of Electrical And Electronics Engineers; Ieee Transactions On Neural Networks; 22; 1; 1-2011; 37-51
dc.identifier.issn 1045-9227
dc.identifier.uri http://hdl.handle.net/11336/15248
dc.description.abstract Many learning problems may vary slowly over time: in particular, some critical real-world applications. When facing this problem, it is desirable that the learning method could find the correct input-output function and also detect the change in the concept and adapt to it. We introduce the time-adaptive support vector machine (TA-SVM), which is a new method for generating adaptive classifiers, capable of learning concepts that change with time. The basic idea of TA-SVM is to use a sequence of classifiers, each one appropriate for a small time window but, in contrast to other proposals, learning all the hyperplanes in a global way. We show that the addition of a new term in the cost function of the set of SVMs (that penalizes the diversity between consecutive classifiers) produces a coupling of the sequence that allows TA-SVM to learn as a single adaptive classifier. We evaluate different aspects of the method using appropriate drifting problems. In particular, we analyze the regularizing effect of changing the number of classifiers in the sequence or adapting the strength of the coupling. A comparison with other methods in several problems, including the well-known STAGGER dataset and the real-world electricity pricing domain, shows the good performance of TA-SVM in all tested situations.
dc.format application/pdf
dc.language.iso eng
dc.publisher Institute Of Electrical And Electronics Engineers
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject ADAPTIVE METHODS
dc.subject DRIFTING CONCEPTS
dc.subject SUPPORT VECTOR MACHINE
dc.subject.classification Otras Ciencias de la Computación e Información
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title Solving nonstationary classification problems with coupled support vector machines
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2017-04-11T17:42:20Z
dc.identifier.eissn 1941-0093
dc.journal.volume 22
dc.journal.number 1
dc.journal.pagination 37-51
dc.journal.pais Estados Unidos
dc.description.fil Fil: Grinblat, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
dc.description.fil Fil: Uzal, Lucas César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
dc.description.fil Fil: Ceccatto, Hermenegildo A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
dc.description.fil Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
dc.journal.title Ieee Transactions On Neural Networks
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TNN.2010.2083684
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/5624639/?tp=&arnumber=5624639


Archivos asociados

Icon
Blocked Acceso no disponible

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)