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• W2108807072 abstract "An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn-Tucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is reversible, and decremental offers an efficient method to exactly evaluate leave-one-out generalization performance. Interpretation of decremental unlearning in feature space sheds light on the relationship between generalization and geometry of the data." @default.
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• W2108807072 date "2000-01-01" @default.
• W2108807072 modified "2023-10-13" @default.
• W2108807072 title "Incremental and Decremental Support Vector Machine Learning" @default.
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