Name
Abstract | ||
|---|---|---|
Classifier combination can be used to combine multiple classification decisions to improve object classification performance, and weighted average is a popular method for this purpose. In this paper we propose to use a graph-theoretic clustering method to define the weights for SVM classifier decisions. Specifically, we use the dominant set clustering to evaluate the difficulty of a kernel matrix for a SVM classifier. This degree of difficulty is found to be related to the SVM classification performance and thus used to define the weight of this classifier. Though simple and intuitive, the method is shown to be as powerful as more sophisticated methods in extensive experiments with several datasets of diverse object types. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288058 | ICASSP |
Keywords | Field | DocType |
classifier combination,graphtheoretic,object classification,weight,support vector machines,accuracy,histograms,weighted average,computer vision,kernel matrix,image classification,graph theory,kernel | Margin (machine learning),Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Margin classifier,Classifier (linguistics),Linear classifier,Cluster analysis,Machine learning,Bayes classifier,Quadratic classifier | Conference |
Volume | Issue | Citations |
null | null | 1 |
PageRank | References | Authors |
0.37 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Hou | 1 | 1 | 1.38 |
| Zhan-Shen Feng | 2 | 6 | 1.58 |
| Bo-ping Zhang | 3 | 10 | 3.65 |