Feature Approach for Printed Document Image Analysis
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Multi-threaded support vector machines for pattern recognition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
| Hi-index | 0.00 |
It has been shown that Support Vector Machine theory optimizes a smoothness functional hypothesis through kernel application. We present KMOD a two - parameter SVM kernel with distinctive properties of good discrimination between patterns while reserving the data neighborhood information. In classification problems the experiments we carried out on the Breast Cancer benchmark produced better performance than RBF kernel and some stat e of the art classifiers. As well it also generated favorable results when subjected to a 10-class problem of recognizing handwritten digits in th e NIST database .