Comparative Study on Classification of Remote Sensing Image by Support Vector Machine

2013 ◽  
Vol 773 ◽  
pp. 893-898 ◽  
Author(s):  
Yong Xu ◽  
Chang Chun Cheng ◽  
Xiao Ming Wang ◽  
Meng Qi Mao ◽  
Chang Jing Zhang

In this paper, the TM image of Landsat-5 was used for classification by the method of support vector machine (SVM). The results and precisions of classification were compared between the different parameter combinations. Further more, precisions are compared between the SVM and traditional algorithm. The results indicate that SVM classification algorithm has the advantage of broad parameters range, without prior knowledge of image and samples. The precision of SVM algorithm is much higher than traditional algorithm, especially adapt to the area without in situ measurement.

2014 ◽  
Vol 548-549 ◽  
pp. 1265-1269
Author(s):  
Yun Sik Hwang ◽  
Byeong Joo Jun ◽  
Tae Seon Yoon

As the stage of bioinformatics has been upgraded, classification of certain pathogen has been improved into a new manner. The main topic of this research is genetic singularity of HCV (Hepatitis C Virus) and our objective is to assay features of the HCV's amino acid under usage of Support Vector Machine (SVM) algorithm. HCV data used in our experiment has 10 kinds of sequences and 257 kinds of data. According to data analysis, some peculiar genetic patterns of HCV’s linearity that discord pre-existing neural network and C5.0 were found.


PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e69434 ◽  
Author(s):  
Xiaomei Zhong ◽  
Jianping Li ◽  
Huacheng Dou ◽  
Shijun Deng ◽  
Guofei Wang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document