Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
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We consider a framework for learning additive classifiers based on regularized empirical risk minimization, where the regularization favors "smooth" functions. We present representations of classifiers for which the optimization problem can be efficiently solved. The first family of such classifiers are derived from a penalized spline formulation due to Eilers and Marx, which is modified to enabled linearization. The second is a novel family of classifiers that are based on classes of orthogonal basis functions with othogonal derivatives. Both these families lead to explicit feature embeddings that can be used with off-the-shelf linear solvers such as LIBLINEAR to obtain additive classifiers. The proposed family of classifiers offer better trade-offs between training time, memory overhead and classifier accuracy, compared to the state-of-the-art in additive classifier training.