---
title: "Adaptive Support Vector Machine for Time-Varying Data Streams Using Martingale"
authors:
  - "Shen-Shyang Ho"
  - "Harry Wechsler"
doi: ""
url: "https://ijcai.org/proceedings/2005/271"
pdf: "https://ijcai.org/proceedings/2005/0271.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "1606-1606"
track: ""
keywords:
---

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

Shen-Shyang Ho, Harry Wechsler

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 1606-1606.

## Abstract

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.