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70 nips-2000-Incremental and Decremental Support Vector Machine Learning


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Author: Gert Cauwenberghs, Tomaso Poggio

Abstract: An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the KuhnTucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decremental


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