A Leap from Randomized to Quantum Clustering with Support Vector Machine - A Computation Complexity Analysis

2020 ◽  
Author(s):  
Arit Kumar Bishwas ◽  
Ashish Mani ◽  
Vasile Palade
Keyword(s):  
Support Vector Machine ◽  
Complexity Analysis ◽  
Support Vector ◽  
Computation Complexity ◽  
Quantum Clustering

Realtime Recognition of Attention State by Complexity Analysis of Electroencephalogram

2013 ◽  
Vol 385-386 ◽  
pp. 1457-1460
Author(s):  
Li Yu Huang ◽  
Hong Juan Ma ◽  
Lang Jin ◽  
Rong Lu
Keyword(s):  
Support Vector Machine ◽  
Complexity Analysis ◽  
Wavelet Packet ◽  
Approximate Entropy ◽  
Support Vector ◽  
Eeg Rhythms ◽  
Average Performance ◽  
Novel Approach ◽  
Eeg Data ◽  
Electroencephalogram Eeg

The object of this paper is presenting a novel approach to classify the attention state and non-attention state. Firstly, the raw recorded electroencephalogram (EEG) data were decomposed by the algorithm of wavelet packet, several main EEG rhythms were extracted; then a complexity measure of these rhythm signal, approximate entropy (ApEn) was calculated respectively, and the values were used as input vector of a trained support vector machine (SVM), the output of this SVM will be the result of classification. The average performance obtained for the proposed scheme in classification is: sensitivity 73.7%, specificity 71.4% and accuracy 72.5%.

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

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
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