Incremental and Decremental Support Vector Machine Learning (2000)
by
Gert Cauwenberghs
,
Tomaso Poggio
| Citations: | 123 - 3 self |
BibTeX
@MISC{Cauwenberghs00incrementaland,
author = {Gert Cauwenberghs and Tomaso Poggio},
title = {Incremental and Decremental Support Vector Machine Learning},
year = {2000}
}
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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 "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. 1
