Adversarial Spam Detection Using the Randomized Hough Transform-Support Vector Machine

2013 ◽  
Author(s):  
Dave Debarr ◽  
Hao Sun ◽  
Harry Wechsler
Keyword(s):  
Support Vector Machine ◽  
Hough Transform ◽  
Support Vector ◽  
Spam Detection ◽  
Randomized Hough Transform

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.

scholarly journals Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1879
Author(s):  
Sangkyeum Kim ◽  
Kyunghyun Lee ◽  
Kwanho You
Keyword(s):  
Support Vector Machine ◽  
Hough Transform ◽  
Laser Interferometer ◽  
Kernel Functions ◽  
P Wave ◽  
Support Vector ◽  
Nonlinearity Error ◽  
Seismic Discrimination ◽  
Heterodyne Laser Interferometer ◽  
Discrimination Method

The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the testing procedure, different kernel functions of SVM are used to discriminate between earthquakes and explosions. The outstanding performance of a laser interferometer and Hough transform method for precision seismic measurement and nonlinearity error compensation is confirmed through some experiments using a linear vibration stage. In addition, the effectiveness of the proposed discrimination method using a heterodyne laser interferometer is verified through a receiver operating characteristic curve and other performance indices obtained from practical experiments.

LSSVM-Based Social Spam Detection Model

2013 ◽  
Vol 765-767 ◽  
pp. 1281-1286
Author(s):  
Xiao Lei Yang ◽  
Yi Dan Su ◽  
Jin Ping Mo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Linear Equations ◽  
Detection Methods ◽  
Support Vector ◽  
Spam Detection ◽  
Squared Error Loss Function ◽  
Detection Model ◽  
Squared Error ◽  
Empirical Function

To Resolve the garbage tag issue in Folksonomy, Lssvm algorithm for social spam detection model (least Squares support vector machine classifiers) was proposed. The method of inequality change the constraints in the traditional support vector machine into equality constraints, and take the empirical function of the squared error loss function as the Experience function in training set. so that the quadratic programming problem convert QP into solving linear equations, it was improving solution the speed of solution and accuracy of convergence.The experimental results show that we have got higher classification accuracyand less predict time than traditional svm detection methods based on least squares support vector machine algorithm garbage tag detection model.

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