Making large-scale support vector machine learning practical
Advances in kernel methods
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Incremental training of support vector machines using hyperspheres
Pattern Recognition Letters
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
A Component-based Framework for Face Detection and Identification
International Journal of Computer Vision
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Object detection problems in computer vision often present a computationally difficult task in machine learning, where very large amounts of high-dimensional image data have to be processed by complex training algorithms We consider training support vector machine (SVM) classifiers on big sets of image data and investigate approximate decomposition techniques that can use any limited conventional SVM training tool to cope with large training sets We reason about expected comparative performance of different approximate training schemes and subsequently suggest two refined training algorithms, one aimed at maximizing the accuracy of the resulting classifier, the other allowing very fast and rough preview of the classifiers that can be expected from given training data We show how the best approximation method trained on an augmented training set of one million perturbed data samples outperforms an SVM trained on the original set.