Incremental training of support vector machines.
We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.
Shilton
Alistair
A
Center of Expertise on Networked Decision and Sensor Systems, Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria 3010, Australia. apsh@ee.mu.oz.au
Palaniswami
M
M
Ralph
Daniel
D
Tsoi
Ah Chung
AC
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D016247:Information Storage and Retrieval / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted