| dc.creator | Schoelkopf, B. | |
| dc.creator | Sung, K. | |
| dc.creator | Burges, C. | |
| dc.creator | Girosi, F. | |
| dc.creator | Niyogi, P. | |
| dc.creator | Poggio, T. | |
| dc.creator | Vapnik, V. | |
| dc.date | 2004-10-20T20:48:54Z | |
| dc.date | 2004-10-20T20:48:54Z | |
| dc.date | 1996-12-01 | |
| dc.date.accessioned | 2013-10-09T02:48:28Z | |
| dc.date.available | 2013-10-09T02:48:28Z | |
| dc.date.issued | 2013-10-09 | |
| dc.identifier | AIM-1599 | |
| dc.identifier | CBCL-142 | |
| dc.identifier | http://hdl.handle.net/1721.1/7180 | |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | |
| dc.description | The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application. | |
| dc.format | 6 p. | |
| dc.format | 2032389 bytes | |
| dc.format | 277809 bytes | |
| dc.format | application/postscript | |
| dc.format | application/pdf | |
| dc.language | en_US | |
| dc.relation | AIM-1599 | |
| dc.relation | CBCL-142 | |
| dc.subject | AI | |
| dc.subject | MIT | |
| dc.subject | Artificial Intelligence | |
| dc.subject | radial basis function networks | |
| dc.subject | support vector machines | |
| dc.subject | pattern recognition | |
| dc.subject | machine learning | |
| dc.subject | VC-dimension | |
| dc.subject | performance comparison | |
| dc.subject | model selection | |
| dc.title | Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers |
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