Online Kernel Learning with a Near Optimal Sparsity Bound
Lijun Zhang, Jinfeng Yi, Rong Jin, Ming Lin, Xiaofei He · 2013
In this work, we focus on Online Sparse Kernel Learning that aims to online learn a kernel classifier with a bounded number of support vectors. Although many online learning algorithms have been proposed to learn a sparse kernel classifier, most of them fail to bound the number of support vectors used by the final solution which is the average of the intermediate kernel classifiers generated byonlinealgorithms. Thekeyideaoftheproposed algorithm is to measure the difficulty in correctly classifying a training example by the derivative of a smooth loss function, and give a more chance to a difficult example to