Title :
On the separability of kernel functions
Author :
Wu, Tao ; He, Hangen ; Hu, Dewen
Author_Institution :
Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha, China
Abstract :
How to select a kernel function for the given data is an open problem in the research of support vector machine (SVM). There is a question puzzling many people: suppose the training data are separated nonlinearly in the input space, how do we know that the chosen kernel function can make the training data to be separated linearly in the feature space? A simple method is presented to decide if a selected kernel function can separate the given data linearly or not in the feature space.
Keywords :
learning (artificial intelligence); support vector machines; SVM; feature space; input space; kernel function separability; support vector machine; training data; Equations; Gene expression; Handwriting recognition; Helium; Kernel; Pattern recognition; Space technology; Sufficient conditions; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198220
