Accurate Streaming Support Vector Machines

TL;DR

This paper introduces a new streaming SVM algorithm based on the minimum enclosing ball formulation, which outperforms existing methods and rivals batch SVM accuracy with efficient online processing.

Contribution

The paper presents a novel streaming SVM algorithm leveraging the MEB formulation and blurred ball cover, improving accuracy and efficiency over prior streaming approaches.

Findings

Outperforms existing streaming SVM algorithms

Achieves higher accuracy than libSVM on several datasets

Competitive with batch SVM in streaming scenarios

Abstract

A widely-used tool for binary classification is the Support Vector Machine (SVM), a supervised learning technique that finds the "maximum margin" linear separator between the two classes. While SVMs have been well studied in the batch (offline) setting, there is considerably less work on the streaming (online) setting, which requires only a single pass over the data using sub-linear space. Existing streaming algorithms are not yet competitive with the batch implementation. In this paper, we use the formulation of the SVM as a minimum enclosing ball (MEB) problem to provide a streaming SVM algorithm based off of the blurred ball cover originally proposed by Agarwal and Sharathkumar. Our implementation consistently outperforms existing streaming SVM approaches and provides higher accuracies than libSVM on several datasets, thus making it competitive with the standard SVM batch implementation.