Name
Abstract | ||
|---|---|---|
Support Vector Machines (SVMs) axe an efficient data mining approach for classification, clustering and time series analysis. In recent years, a tremendous growth in the amount of data gathered has changed the focus of SVM classifier algorithms from providing accurate results to enabling incremental (and decremental) learning with new data (or unlearning old data) without the need for computationally costly retraining with the old data. In this paper we propose an efficient algorithm for multicategory classification with the incremental proximal SVM introduced by Fung and Mangasarian. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45224-9_54 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
data gathering,time series analysis,support vector machine | Time series,Computer science,Support vector machine,Multicategory,Artificial intelligence,Svm classifier,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
2773 | 0302-9743 | 8 |
PageRank | References | Authors |
0.56 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amund Tveit | 1 | 78 | 7.43 |
| Magnus Lie Hetland | 2 | 73 | 8.04 |