Safety Application Car Crash Detection Using Multiclass Support Vector Machine

2021 ◽  
Author(s):  
Mike Schwarz ◽  
Alexander Buhmann
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Car Crash ◽  
Multiclass Support Vector Machine ◽  
Safety Application

Agarwood Oil Quality Grading using OVO Multiclass Support Vector Machine

2021 ◽  
Author(s):  
A. F. M. Amidon ◽  
N. Z. Mahabob ◽  
M. H. Haron ◽  
N. Ismail ◽  
Z. M. Yusoff ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Oil Quality ◽  
Support Vector ◽  
Quality Grading ◽  
Multiclass Support Vector Machine ◽  
Agarwood Oil

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

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