Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels

Automatic model selection for the optimi

Automatic model selection for the optimization of SVM kernels

✍ N.E. Ayat; M. Cheriet; C.Y. Suen 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 349 KB

This approach aims to optimize the kernel parameters and to efficiently reduce the number of support vectors, so that the generalization error can be reduced drastically. The proposed methodology suggests the use of a new model selection criterion based on the estimation of the probability of error