Name
Abstract | ||
|---|---|---|
A large number of training samples is requiredin developing visual object recognition systems. However, the size of samples is limited sometimes. This paper investigates bagging of one class support vector machines (OCSVM), which just use one class of objects for training. Experiments are performed on Caltech101 database. Our findings show that the performance with bagging method is better than single OCSVM. Furthermore, bagging of OCSVM can also keep better performance with limited number of training samples. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IBICA.2011.29 | IBICA |
Keywords | Field | DocType |
training sample,visual object recognition,class support vector machine,class support vector machines,large number,better performance,visual boject recognition,one class support vector machines,visual object recognition system,object recognition,caltech101 database,single ocsvm,bagging,visual object recognition systems,support vector machines,bagging method,limited number,visualization,computer vision,support vector machine,kernel | Kernel (linear algebra),Pattern recognition,Computer science,Visualization,Support vector machine,Artificial intelligence,Machine learning,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
978-1-4577-1219-7 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongxia Xie | 1 | 683 | 23.38 |
| Yong Xu | 2 | 9 | 9.53 |
| Qinghua Hu | 3 | 4028 | 171.50 |