The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In the conventional regularized learning, training time increases as the training set expands. Recent work on L2 linear SVM challenges this common sense by proposing the inverse time dependency on the training set size. In this paper, we first put forward a Primal Gradient Solver (PGS) to effectively solve the convex regularized learning problem. This solver is based on the stochastic gradient descent...
It is an extreme challenge to produce a nonlinear SVM classifier on very large scale data. In this paper we describe a novel P-packSVM algorithm that can solve the support vector machine (SVM) optimization problem with an arbitrary kernel. This algorithm embraces the best known stochastic gradient descent method to optimize the primal objective, and has 1/?? dependency in complexity to obtain a solution...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.