scholarly journals Multi-class Classification Method of Support Vector Machine Based on Error Correction Coding

2021 ◽  
Author(s):  
Li Junfei ◽  
Zhao Longhai
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Classification Method ◽  
Radiation Environment ◽  
Support Vector ◽  
Single Event ◽  
Error Correction Coding ◽  
Multi Class Classification ◽  
Multi Classification ◽  
Space Radiation Environment

Abstract In the space radiation environment, there will be many errors in the multi-classification results of support vector machine which caused by single event flipping , the ability of correcting classification errors through error correction coding is studied in this paper, results of simulation confirm that error correction coding can increase the accuracy ,which is beneficial for anti-single event flip.

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.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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