Name
Abstract | ||
|---|---|---|
A novel transfer support vector machine called TSVM-GP with group probabilities is proposed for the scenarios where plenty of labeled data in the source domain and the group probabilities of unlabeled data in the target domain are available. TSVM-GP integrates a transfer term and group probabilities into the support vector machine (SVM) to improve the classification accuracy. In order to reduce the high computational complexity of TSVM-GP, the scalable version of TSVM-GP called scalable transfer support vector machine with group probabilities (STSVM-GP) is further developed by selecting the representative set of the training samples as the training data in the source domain. Experimental results on synthetic datasets as well as several real-world datasets show the effectiveness of the proposed classifiers, and especially STSVM-GP is very feasible for large scale transfer datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2017.08.049 | Neurocomputing |
Keywords | Field | DocType |
Large datasets,Classification,Support vector machine,Transfer learning,Group probability | Structured support vector machine,Data mining,Computer science,Transfer of learning,Artificial intelligence,Labeled data,Training set,Pattern recognition,Support vector machine,Relevance vector machine,Machine learning,Scalability,Computational complexity theory | Journal |
Volume | ISSN | Citations |
273 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 25 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongguang Ni | 1 | 16 | 6.31 |
| Xiaoqing Gu | 2 | 44 | 9.30 |
| Jun Wang | 3 | 152 | 9.49 |
| Yuhui Zheng | 4 | 16 | 3.28 |
| Hongyuan Wang | 5 | 2 | 4.42 |