Intelligentized evaluation on dynamic characteristics of CO2 arc welding power source based on key Eigen values and support vector machine

2011 ◽  
Author(s):  
Liwen Gao ◽  
Jiaxiang Xue ◽  
Fang Lin ◽  
Wen Zhang ◽  
Hui Chen
Keyword(s):  
Support Vector Machine ◽  
Dynamic Characteristics ◽  
Arc Welding ◽  
Power Source ◽  
Support Vector ◽  
Eigen Values

Research on Lateral Aerodynamic Modeling Based on Relevance Vector Machine

2013 ◽  
Vol 327 ◽  
pp. 306-309
Author(s):  
Xu Sheng Gan ◽  
Jing Shun Duanmu ◽  
Jian Guo Gao
Keyword(s):  
Support Vector Machine ◽  
Dynamic Characteristics ◽  
Basis Function ◽  
Bayesian Learning ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Flight Vehicle ◽  
Learning Method ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model

To describe the dynamic characteristics of flight vehicle accurately, a Relevance Vector Machine (RVM) aerodynamic modeling method is proposed. RVM is a learning method which is based on Bayesian learning theory. Compared with Support Vector Machine (SVM), it has the benefits such as probabilistic predictions, sparser model, facilities to select arbitrary basis function and so on. Experimental results show that the proposed method can obtain the aerodynamic model with higher accuracy by less relevance vector. It is also effective and feasible for aerodynamic modeling.

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.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
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