Name
Abstract | ||
|---|---|---|
Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of the proposed Fuzzy SVM compared to the traditional SVM. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FUZZ-IEEE.2012.6251186 | 2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) |
Keywords | Field | DocType |
fuzzy suppor vector machines, membership function, image classification, self-organizing maps | Pattern recognition,Computer science,Support vector machine,Fuzzy logic,Semantic gap,Fuzzy set,Self-organizing map,Artificial intelligence,Contextual image classification,Decision boundary,Membership function,Machine learning | Conference |
ISSN | Citations | PageRank |
1098-7584 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umit Lutfu Altintakan | 1 | 0 | 0.68 |
| Adnan Yazici | 2 | 649 | 56.29 |
| Murat Koyuncu | 3 | 89 | 10.32 |