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NIPS
2000

Incremental and Decremental Support Vector Machine Learning

13 years 11 months ago
Incremental and Decremental Support Vector Machine Learning
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 "unlearning" 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.
Gert Cauwenberghs, Tomaso Poggio
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where NIPS
Authors Gert Cauwenberghs, Tomaso Poggio
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