scholarly journals Gender Classification from ECG Signal Analysis using Least Square Support Vector Machine

2012 ◽  
Vol 2 (5) ◽  
pp. 145-149 ◽  
Author(s):  
Rajesh Ku. Tripathy ◽  
Ashutosh Acharya ◽  
Sumit Kumar Choudhary
Keyword(s):  
Support Vector Machine ◽  
Signal Analysis ◽  
Least Square ◽  
Support Vector ◽  
Ecg Signal ◽  
Gender Classification

Support Vector Machine Based Sub-Classification of Arrhythmia Using ECG Signal

2020 ◽  
Author(s):  
Mohit Singh Dhaka ◽  
Poras Khetarpal ◽  
Neeraj Kumar
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Ecg Signal

Authentication through gender classification from iris images using support vector machine

2021 ◽  
Author(s):  
Amjad Rehman Khan ◽  
Fatemeh Doosti ◽  
Mohsen Karimi ◽  
Majid Harouni ◽  
Usman Tariq ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Gender Classification

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

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