Multiple-Instance Learning Support Vector Machine Algorithm based Pedestrian Detection

2020 ◽  
Author(s):  
T Haritha. ◽  
Rajesh Kannan Megalingam
Keyword(s):  
Support Vector Machine ◽  
Pedestrian Detection ◽  
Multiple Instance Learning ◽  
Support Vector ◽  
Learning Support ◽  
Support Vector Machine Algorithm

Detection of Breast Cancer Using Machine Learning Support Vector Machine Algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 441-444
Author(s):  
D. V. Soundari ◽  
R. Padmapriya ◽  
C. Thirumariselvi ◽  
N. Nanthini ◽  
K. Priyadharsini
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Support ◽  
Support Vector Machine Algorithm ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Hormone Imbalance

A woman majorly suffers due to breast cancer which is due to hormone imbalance. It leads to huge death in recent years. Early detection of the breast cancer is more important to prevent human lives. Image Processing plays an important to classify and detect the same. So this paper proposes machine learning based cancer classification using support vector machine with Wisconsin breast cancer data set.

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
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