The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient method for simplifying support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
An Efficient Method for Simplifying Decision Functions of Support Vector Machines
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Fast evaluation of neural networks via confidence rating
Neurocomputing
Journal of Cognitive Neuroscience
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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.