Name
Abstract | ||
|---|---|---|
Kernel based methods are significantly important in the pattern classification problem, especially when different classes are not linearly separable. In this paper, we propose a new kernel, which is the modified version of the polynomial kernel. The free parameter (d) of the proposed kernel considerably affects the error rate of the classifier. Thus, we present a new algorithm based on the Fisher criterion to find the optimum value of d. Simulation results show that using the proposed kernel for classification leads to satisfactory results. In our simulation in most cases the proposed method outperforms the classification using the polynomial kernel. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/MLSP.2011.6064561 | 2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) |
Keywords | DocType | ISSN |
Pattern classification, kernel based methods, kernel learning, polynomial kernel | Conference | 2161-0363 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elham Taghizadeh | 1 | 0 | 0.34 |
| Zahra Sadeghipoor | 2 | 27 | 3.61 |
| Mohammad Taghi Manzuri | 3 | 18 | 4.78 |