Human Age Estimation Using Support Vector Machine

2021 ◽  
pp. 273-286
Author(s):  
A. Madhavi ◽  
G. Bhuvana Sree ◽  
V. Shriya ◽  
B. Shanmukh ◽  
T. Harshitha
Keyword(s):  
Support Vector Machine ◽  
Age Estimation ◽  
Support Vector ◽  
Human Age Estimation

Age estimation based on speech features and support vector machine

2011 ◽  
Author(s):  
Davood Mahmoodi ◽  
Hossein Marvi ◽  
Mehdi Taghizadeh ◽  
Ali Soleimani ◽  
Farbod Razzazi ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Age Estimation ◽  
Support Vector ◽  
Speech Features

Age Estimation Using Support Vector Machine–Sequential Minimal Optimization

2015 ◽  
Vol 2 (2) ◽  
Author(s):  
Julianson Berueco ◽  
Kim Lopena ◽  
Arby Moay ◽  
Mehdi Salemiseresht ◽  
Chuchi Montenegro
Keyword(s):  
Support Vector Machine ◽  
Age Estimation ◽  
Support Vector ◽  
Sequential Minimal Optimization

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
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