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. © 2011 Springer-Verlag.
CITATION STYLE
Déniz-Suárez, O., Castrillón, M., Lorenzo, J., Bueno, G., & Hernández, M. (2011). Fast classification in incrementally growing spaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6669 LNCS, pp. 305–312). https://doi.org/10.1007/978-3-642-21257-4_38
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