Name
Abstract | ||
|---|---|---|
Cost-sensitive learning has been a hot research topic in machine learning. Many cost-sensitive methods have been successfully applied in many real-world applications such as disease diagnosis, fraud detection and business decision making. In this paper, we proposed a new Cost-Sensitive Laplacian Support Vector Machine(called Cos-LapSVM), which can deal with the cost-sensitive problem in Semi-Supervised Learning. The effectiveness of the proposed method is demonstrated via experiments on UCI datasets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.procs.2013.05.336 | 2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE |
Keywords | Field | DocType |
SVM, Cost-Sensitive, Semi-Supervised Learning | Structured support vector machine,Online machine learning,Data mining,Semi-supervised learning,Instance-based learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Relevance vector machine,Machine learning | Conference |
Volume | ISSN | Citations |
18 | 1877-0509 | 8 |
PageRank | References | Authors |
0.43 | 26 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiquan Qi | 1 | 665 | 29.41 |
| Yingjie Tian | 2 | 807 | 58.32 |
| Yu Shi | 3 | 3208 | 264.97 |
| Xiaodan Yu | 4 | 33 | 4.31 |