Online training on a budget of support vector machines using twin prototypes

: This paper proposes twin prototype support vector machine (TVM), a constant space and sublinear time support vector machine (SVM) algorithm for online learning. TVM achieves its favorable scaling by memorizing only a fixed-size data summary in the form of example prototypes and their associated information during training. In addition, TVM guarantees that the optimal SVM solution is maintained on all prototypes at any time. To maximize the accuracy of TVM, prototypes are constructed to approximate the data distribution near the decision boundary. Given a new training example, TVM is updated in three steps. First, the new example is added as a new prototype if it is near the decision boundary. If this happens, to maintain the budget, either the prototype farthest away from the decision boundary is removed or two near prototypes are selected and merged into a single one. Finally, TVM is updated by incremental and decremental techniques to account for the change. Several methods for pr...

• Type: Journal
• Year: 2010
• Where: SADM
• Authors: Zhuang Wang, Slobodan Vucetic
• Keywords: Decision Boundary, SADM 2010, Support Vector Machine, TVM Accuracy