Name
Abstract | ||
|---|---|---|
Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a cost-sensitive perceptron learning rule for non-separable classes, that can be extended to multi-modal classes (DIPOL) and present a natural cost-sensitive extension of the support vector machine (SVM). |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45231-7_16 | ADVANCES IN INTELLIGENT DATA ANALYSIS V |
Keywords | Field | DocType |
support vector machine,machine learning | Online machine learning,Semi-supervised learning,Active learning (machine learning),Pattern recognition,Computer science,Support vector machine,Learning rule,Artificial intelligence,Computational learning theory,Artificial neural network,Perceptron,Machine learning | Conference |
Volume | ISSN | Citations |
2810 | 0302-9743 | 2 |
PageRank | References | Authors |
0.37 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Geibel | 1 | 286 | 26.62 |
| Ulf Brefeld | 2 | 633 | 51.89 |
| Fritz Wysotzki | 3 | 456 | 45.46 |