Fast Classification in Incrementally Growing Spaces [chapter]

Oscar Déniz-Suárez, Modesto Castrillón, Javier Lorenzo, Gloria Bueno, Mario Hernández
2011 Lecture Notes in Computer Science  
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
more » ... 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.
doi:10.1007/978-3-642-21257-4_38 fatcat:dgok7e4a5jaixbcikzp3c7eulu