Incremental support vector learning: analysis, implementation and applications
- Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.
| Author details: | Pavel LaskovGND, Christian GehlORCiD, Stefan Krüger, Klaus-Robert MüllerORCiDGND |
|---|---|
| ISSN: | 1532-4435 |
| Title of parent work (English): | Journal of machine learning research |
| Publisher: | MIT Press |
| Place of publishing: | Cambridge, Mass. |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2006/09/07 |
| Publication year: | 2006 |
| Release date: | 2020/06/02 |
| Tag: | drug discovery; incremental SVM; intrusion detection; online learning |
| Volume: | 7 |
| Number of pages: | 28 |
| First page: | 1909 |
| Last Page: | 1936 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
| Peer review: | Referiert |
| Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |
