@inproceedings{cauwenbergs01incrementaldecremental,
title = {Incremental and Decremental Support Vector Machine Learning},
author = {G. Cauwenberghs and T. Poggio},
booktitle = {Advances in Neural Information Processing Systems (NIPS*2000)},
url = {http://bach.ece.jhu.edu/pub/gert/papers/nips00_inc.pdf},
volume = {13},
year = {2001},
description = {KDubiq Blueprint},
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 ``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. },
groupsearch = {0},
keywords = {Blueprint KDubiq kdubiq }
}
::