Evaluating the VNIR-SWIR datasets of WorldView-3 for lithological mapping of a metamorphic-igneous terrain using Support Vector Machine algorithm; a case study of Central Iran

2021 ◽  
Author(s):  
Sogand Karimzadeh ◽  
Majid H. Tangestani
Keyword(s):  
Support Vector Machine ◽  
Central Iran ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Lithological Mapping

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

Prediction of solubility of carbon dioxide in different polymers using support vector machine algorithm

2015 ◽  
Vol 46 ◽  
pp. 205-213 ◽  
Author(s):  
Hossein Ziaee ◽  
Seyyed Mohsen Hosseini ◽  
Abdolmajid Sharafpoor ◽  
Mohammad Fazavi ◽  
Mohammad Mahdi Ghiasi ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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