scholarly journals Robust Multi-Weight Vector Projection Support Vector Machine

Author(s):  
Heng-hao ZHAO ◽  
Qiao-lin YE
2018 ◽  
Vol 315 ◽  
pp. 345-361 ◽  
Author(s):  
Wei-Jie Chen ◽  
Chun-Na Li ◽  
Yuan-Hai Shao ◽  
Ju Zhang ◽  
Nai-Yang Deng

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 3275-3286 ◽  
Author(s):  
Henghao Zhao ◽  
Qiaolin Ye ◽  
Meen Abdullah Naiem ◽  
Liyong Fu

2011 ◽  
Vol 38 (1) ◽  
pp. 856-861 ◽  
Author(s):  
Renbing Li ◽  
Aihua Li ◽  
Tao Wang ◽  
Liang Li

2010 ◽  
Vol 31 (13) ◽  
pp. 2006-2011 ◽  
Author(s):  
Qiaolin Ye ◽  
Chunxia Zhao ◽  
Ning Ye ◽  
Yannan Chen

2011 ◽  
Vol 121-126 ◽  
pp. 4892-4896
Author(s):  
Ye Cai Guo ◽  
Zhi Chao Zhang ◽  
Fang Xu ◽  
Shi Jie Guo

In order to overcome the contradiction of the CMA with a constant step-size between the convergence rate and the residual mean square error(MSE), on the basis of analyzing the idea of variable step-size, the feature of Support Vector Machine(SVM) and Wavelet Transform, a Variable step-size Wavelet transform Support vector machine Constant Modulus blind equalization Algorithm (VWSCMA) is proposed. In the proposed algorithm, the variable step-size is used to solve the contradiction between the convergence rate and the residual MSE, SVM is employed to optimize the weight vector of equalizer, and wavelet transform is used to reduce the autocorrelation of input signals of equalizer. Simulation results show that the proposed algorithm can effectively overcome the contradiction between the convergence rate and the residual error and has good equalization performance.


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

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede

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.


Sign in / Sign up

Export Citation Format

Share Document