Referenced Items are not available for this document.
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.
Published in:
Innovations in Bio-inspired Computing and Applications (IBICA), 2011 Second International Conference on
Date of Conference: 16-18 Dec. 2011
- Page(s):
- 99 - 102
- Print ISBN:
- 978-1-4577-1219-7
- INSPEC Accession Number:
- 12526512
- Conference Location :
- Shenzhan
- Digital Object Identifier :
- 10.1109/IBICA.2011.29
- Product Type:
- Conference Publications
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