Adaptive support vector machine for time-varying data streams using martingale
Shen-Shyang Ho, Harry Wechsler · International Joint Conference on Artificial Intelligence · 2005
A martingale framework is proposed to enable support vector machine (SVM) to adapt to timevarying data streams. The adaptive SVM is a onepass incremental algorithm that (i) does not require a sliding window on the data stream, (ii) does not require monitoring the performance of the classifier as data points are streaming, and (iii) works well for high dimensional, multi-class data streams. Our experiments show that the novel adaptive SVM is effective at handling time-varying data streams simulated using both a synthetic dataset and a multiclass real dataset.