Detecting RNA sequences using two-stage SVM classifier

被引：0
|
作者：
Li, Xiaoou [1 ]
Li, Kang [2 ]
机构：
[1] CINVESTAV, IPN, Dept Comput, AP 14-740,Av IPN 2508, Mexico City 07360, DF, Mexico
[2] Queens Univ Belfast, Schl Elect, Dept Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland
来源：
LIFE SYSTEM MODELING AND SIMULATION, PROCEEDINGS | 2007年 / 4689卷
关键词：
D O I：
10.1007/978-3-540-74771-0_2
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
RNA sequences detection is time-consuming because of its huge data set size. Although SVM has been proved to be useful, normal SVM is not suitable for classification of large data sets because of its high training complexity. A two-stage SVM classification approach is introduced for fast classifying large data sets. Experimental results on several RNA sequences detection demonstrate that the proposed approach is promising for such applications.
引用
收藏
页码：8 / +
页数：4
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