Dual-nu support vector machines and applications in multi-class image recognition
Hong Gunn Chew, Cheng‐Chew Lim, Robert E. Bogner · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · 2004
Dual-nu Support Vector Machine (SVM) is an effective method in pattern recognition and target detection. It offers competitive performance in detection and computation with traditional classifiers. In this paper, we show that the Dual-nu SVM is capable of achieving classification performance for binary classification no worse than other types of Support Vector Machines, including C-SVM and nu-SVM. We investigate the use of Dual-nu SVM in multi-class image recognition using the winnertakes- all rejection strategy. Performance of Dual-nu SVM on a 60,000-element training set and 10,000-element test set handwritten digit recognition problem is analysed.