Adaptive Support Vector Machine for Time-Varying Data Streams Using Martingale

Introduction In this paper we propose an efficient adaptive support vector machine (SVM) for time-varying data streams based on the martingale approach [2] and using adiabatic incremental learning [1]. When a new data point is observed, hypothesis testing decides whether any change has occurred. Once a change is detected, historical information about previous data is removed from the memory. The adaptive support vector machine is a one-pass incremental algorithm that 1. does not require a sliding window on the data stream, 2. does not require monitoring the performance of the classifier as data points are streaming, and 3. works well for high dimensional, multi-class data streams.