Title :
Applied research in iatrology classification based on SVM
Author_Institution :
Sch. of Math. & Comput., Ningxia Univ., Yinchuan, China
Abstract :
In the paper we introduced the soft margin SVC to solve linearly inseparable problems. Compared with the kernel trick, it is obvious that the two approaches actually solve the problems in different manners. Then we provided a novel view to design a kernel function based on a general proximity relation mapping. It shows better classification performance than the common Mercer kernels experimentally in the iatrology area.
Keywords :
biomedical engineering; data mining; medical computing; pattern classification; support vector machines; SVM based iatrology classification; general proximity relation mapping; kernel function; linearly inseparable problems; soft margin SVC; Classification algorithms; Equations; Kernel; Static VAr compensators; Support vector machines; Training; Vectors; SVM(Support vector machines); classifier; data mining algorithms;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9351-7
DOI :
10.1109/BMEI.2011.6098738
