Enhanced M-ary support vector machine by error correction coding for multi-category classification

2013 ◽  
Vol 32 (3) ◽  
pp. 661-664
Author(s):  
Jian BAO ◽  
Ran LIU
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Support Vector ◽  
Error Correction Coding

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.

Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction

2009 ◽  
Vol 56 (2) ◽  
pp. 336-344 ◽  
Author(s):  
Shi-Yun Shao ◽  
Kai-Quan Shen ◽  
Chong Jin Ong ◽  
E. Wilder-Smith ◽  
Xiao-Ping Li
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Support Vector ◽  
Artifact Removal

A Bayesian nonlinear support vector machine error correction model

2006 ◽  
Vol 25 (2) ◽  
pp. 77-100 ◽  
Author(s):  
Tony Van Gestel ◽  
Marcelo Espinoza ◽  
Bart Baesens ◽  
Johan A. K. Suykens ◽  
Carine Brasseur ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Error Correction Model ◽  
Support Vector ◽  
Machine Error ◽  
Correction Model ◽  
Nonlinear Support

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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