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.

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Gert Cauwenberghs, Tomaso Poggio
• Keywords: Leave-one-out Generalization Performance, NIPS 2000, NIPS 2007, On-line Recursive Algorithm, Training Support Vector