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Title: Fast classification in incrementally growing spaces
Authors: Déniz Suárez,Oscar 
Castrillón-Santana, Modesto 
Lorenzo Navarro, José Javier 
Bueno, Gloria
Hernández Tejera, Francisco Mario 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Gender recognition
Support vector machines
Principal component analysis
Issue Date: 2011
Project: Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. 
Journal: Lecture Notes in Computer Science 
Conference: 5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA) 
Abstract: The classification speed of state-of-the-art classifiers such as SVM is an important aspect to be considered for emerging applications and domains such as data mining and human-computer interaction. Usually, a test-time speed increase in SVMs is achieved by somehow reducing the number of support vectors, which allows a faster evaluation of the decision function. In this paper a novel approach is described for fast classification in a PCA+SVM scenario. In the proposed approach, classification of an unseen sample is performed incrementally in increasingly larger feature spaces. As soon as the classification confidence is above a threshold the process stops and the class label is retrieved. Easy samples will thus be classified using less features, thus producing a faster decision. Experiments in a gender recognition problem show that the method is by itself able to give good speed-error tradeoffs, and that it can also be used in conjunction with other SV-reduction algorithms to produce tradeoffs that are better than with either approach alone.
ISBN: 978-3-642-21256-7
ISSN: 0302-9743
DOI: 10.1007/978-3-642-21257-4_38
Source: Pattern Recognition And Image Analysis: 5Th Iberian Conference, Ibpria 2011[ISSN 0302-9743],v. 6669, p. 305-312, (2011)
Appears in Collections:Actas de congresos
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