One-pass online SVM with extremely small space complexity
Yangwei Liu, Jinhui Xu · 2016
In this paper we consider the problem of training a Support Vector Machine (SVM) online using a stream of data in random order. We provide a fast online training algorithm for general SVM on very large datasets. Based on the geometric interpretation of SVM known as the polytope distance, our algorithm uses a gradient descent procedure to solve the problem. With high probability our algorithm outputs an (ε; δ)-approximation result in constant time and space, which is independent of the size of the dataset, where (ε; δ)-approximation means that the separating margin of the classifier is almost optimal (with error ≤ ε), and the number of misclassified training points is very small (with error ≤ δ). Experimental results show that our algorithm outperforms most of existing online algorithms, especially in the space requirement aspect, while maintaining high accuracy.