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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15858
Title: A new boosting design of Support Vector Machine classifiers
Authors: Mayhua López, Efraín
Gómez Verdejo, Vanessa
Figueiras Vidal, Aníbal
Keywords: Adaptive boosting;Algorithms;Design;Iterative methods;Linear programming;Structure (composition);Boosting algorithm;Computational effort;Ensemble classifiers;Monolithic architecture;Performance design;Subsampling;Support vector machine (SVMs);Support vector machine classifiers;Support vector machines
Issue Date: 2015
Publisher: Elsevier
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924807118&doi=10.1016%2fj.inffus.2014.10.005&partnerID=40&md5=d6871508ff8878cf2929a6a050dd3c42
Abstract: Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the resulting ensemble can be easily compacted into a monolithic architecture by simply combining the weights that correspond to the same kernels when they appear in different learners, avoiding to increase the operation computational effort for the above potential advantage. This way, the performance advantage that boosting provides can be obtained for monolithic SVMs, i.e., without paying in classification computational effort because many learners are needed. However, SVMs are both stable and strong, and their use for boosting requires to unstabilize and to weaken them. Yet previous attempts in this direction show a moderate success. In this paper, we propose a combination of a new and appropriately designed subsampling process and an SVM algorithm which permits sparsity control to solve the difficulties in boosting SVMs for obtaining improved performance designs. Experimental results support the effectiveness of the approach, not only in performance, but also in compactness of the resulting classifiers, as well as that combining both design ideas is needed to arrive to these advantageous designs. © 2014 Elsevier B.V.All rights reserved.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15858
ISSN: 15662535
Appears in Collections:Artículos - Ciencia de la computación

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