Adaptive Support Vector Machine for Time-Varying Data Streams Using Martingale
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.
@inproceedings{ijcai2005p271,
title = {Adaptive Support Vector Machine for Time-Varying Data Streams Using Martingale},
author = {Ho, Shen-Shyang and Wechsler, Harry},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1606--1606},
year = {2005},
url = {https://ijcai.org/proceedings/2005/271},
}
TY - CPAPER TI - Adaptive Support Vector Machine for Time-Varying Data Streams Using Martingale AU - Ho, Shen-Shyang AU - Wechsler, Harry T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1606 EP - 1606 UR - https://ijcai.org/proceedings/2005/271 ER -Copied
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