Name
Title | ||
|---|---|---|
Empirical Error based Optimization of SVM Kernels: Application to Digit Image Recognition |
Abstract | ||
|---|---|---|
We address the problem of optimizing kernel parameters in support vector machine modeling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced kernel. The present work is an extended experimental study of the framework proposed by Chapelle et al. (2001) for optimizing SVM kernels using an analytic upper bound of the error. However our optimization scheme minimizes an empirical error estimate using a quasi-Newton optimization method. To assess our method, the approach is further used for adapting KMOD, RBF and polynomial kernels on synthetic data and NIST database. The method shows a much faster convergence with satisfactory results in comparison with the simple gradient descent method. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/IWFHR.2002.1030925 | IWFHR |
Keywords | Field | DocType |
svm kernels,empirical error estimate,quasi-newton optimization method,nist database,optimizing kernel parameter,digit image recognition,polynomial kernel,optimization scheme,empirical error,support vector machine modeling,simple gradient descent method,optimizing svm,extended experimental study,upper bound,support vector machine,polynomials,probability,gradient descent method,optimization,convergence,image classification,support vector machines,digital image,synthetic data,image recognition,kernel,nist,databases | Kernel (linear algebra),Gradient descent,Pattern recognition,Polynomial,Computer science,Upper and lower bounds,Support vector machine,Synthetic data,NIST,Artificial intelligence,Contextual image classification,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-1692-0 | 6 | 0.79 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayat, N.E. | 1 | 85 | 5.14 |
| Cheriet, M. | 2 | 29 | 2.42 |
| Ching Y. Suen | 3 | 7569 | 1127.54 |