Abstract
A martingale framework is proposed to enable support vector machine (SVM) to adapt to time-varying data streams. The adaptive SVM is a one-pass 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.
Original language | English (US) |
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Pages (from-to) | 1606-1607 |
Number of pages | 2 |
Journal | IJCAI International Joint Conference on Artificial Intelligence |
State | Published - 2005 |
Externally published | Yes |
Event | 19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom Duration: Jul 30 2005 → Aug 5 2005 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence