Real-Time Foreground-Background Segmentation Using Adaptive Support Vector Machine Algorithm

2007 ◽  
pp. 603-610 ◽  
Author(s):  
Zhifeng Hao ◽  
Wen Wen ◽  
Zhou Liu ◽  
Xiaowei Yang
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Background Segmentation ◽  
Adaptive Support

scholarly journals Support Vector Machine Algorithm for Real-Time Detection of VF Signals

2011 ◽  
Vol 8 ◽  
pp. 602-608
Author(s):  
Chunyun Zhang ◽  
Jie Zhao ◽  
Fei Li ◽  
Huilin Jia ◽  
Jie Tian
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Real Time Detection

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.

Lipid profiling using Raman and a modified support vector machine algorithm

2021 ◽  
Author(s):  
Mariana C. Potcoava ◽  
Gregory L. Futia ◽  
Emily A. Gibson ◽  
Isabel R. Schlaepfer
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Lipid Profiling
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