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Additional info for New Algorithms for Large-Scale Support Vector Machines
48 48 52 53 54 54 55 56 61 large scale training sets are involved, Stochastic Gradient Descent (SGD) algorithms W hen are usually one of the best ways to take advantage of all the data. Indeed, when the bottleneck is the computing time rather than the number of training examples, [Bottou and Bousquet, 2008] established that SGD often yields the best generalization performances, in spite of being poor optimizers.
In both cases, LaRank shares the generalization performances of batch optimizers and the speed of standard online methods. Theoretical Study Every derivation is proved to eventually converge to the same solution as batch methods by theoretical proofs spread in the chapters. 4, we provide a theoretical study of the Process/Reprocess principle in the context of online approximate optimization. We analyse a simple algorithm for SVMs for binary classification, and show that a constant number of Reprocess operations is sufficient to maintain, on the course of the algorithm, an averaged accuracy criterion, with a computational cost that scales as well as the best existing SVMs algorithms with the number of examples.
34 34 37 39 41 42 42 45 46 46 n this thesis, we address the training of Support Vector Machines (SVMs) on large scale databases. SVMs [Vapnik, 1998] are supervised learning methods originally used for binary Iclassification and regression. , 1964] to large margin classifiers [Vapnik and Lerner, 1963] and have proved to be powerful tools. 1 The contributions of this dissertation cover the general framework of SVMs. Hence, their applicative scope is potentially very vast.
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