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Online-batch strongly convex Multi Kernel Learning
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-theart performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate. Here we present a Multiclass Multi Kernel Learning (MKL) algorithm that obtains
doi:10.1109/cvpr.2010.5540137
dblp:conf/cvpr/OrabonaJC10
fatcat:fx23gqyq3ffgnkpduhkkt4rpkq