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Online-Batch Strongly Convex Multi Kernel Learning

Online-Batch Strongly Convex Multi Kernel Learning

This video was recorded at 23rd IEEE Conference on Computer Vision and Pattern Recognition 2010 - San Francisco. 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 state-of-the-art performance in a considerably lower training time. We generalize the standardMKL formulation to introduce a parameter that allows us to decide the level of sparsity of the solution.... Show More

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