Neighborhood Based SVM Multi-classification Method for Condition Assessment of Insulator

2011 ◽  
Author(s):  
Nian Du ◽  
Yongli Zhu
Keyword(s):  
Condition Assessment ◽  
Classification Method ◽  
Multi Classification

Application of multi-classification method of skin cancer based on dermoscopic image

2021 ◽  
Author(s):  
Qian Chen ◽  
Min Li ◽  
Cheng Chen ◽  
Chen Chen ◽  
Xiaoyi Lv
Keyword(s):  
Skin Cancer ◽  
Classification Method ◽  
Dermoscopic Image ◽  
Multi Classification

scholarly journals A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

2016 ◽  
Vol 28 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Hongzhuan Zhao ◽  
Dihua Sun ◽  
Min Zhao ◽  
Senlin Cheng
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Binary Tree ◽  
Classification Method ◽  
Traffic Information ◽  
Support Vector ◽  
Cyber Physical System ◽  
Svm Classification ◽  
Forecasting Performance ◽  
Multi Classification

With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

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