Back analysis of displacement based on support vector machine and continuous tabu search

2011 ◽  
Author(s):  
Fei Xu ◽  
Ke Wang ◽  
Jidong Su ◽  
Zheng Xiong ◽  
Guilan Liang
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Back Analysis ◽  
Support Vector ◽  
Displacement Based

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope

2016 ◽  
Vol 203 ◽  
pp. 178-190 ◽  
Author(s):  
Shaojun Li ◽  
Hongbo Zhao ◽  
Zhongliang Ru ◽  
Qiancheng Sun
Keyword(s):  
Support Vector Machine ◽  
Rock Slope ◽  
Back Analysis ◽  
Support Vector

Inverse Analysis of Surrounding Rock Mechanical Parameters Based on LS-SVM

2011 ◽  
Vol 422 ◽  
pp. 547-550
Author(s):  
Xiao Long Li ◽  
Fu Ming Wang ◽  
Yan Hui Zhong ◽  
Cheng Chao Guo
Keyword(s):  
Support Vector Machine ◽  
Inverse Analysis ◽  
Back Analysis ◽  
Surrounding Rock ◽  
Support Vector ◽  
Inversion Method ◽  
Mechanical Parameters ◽  
Underground Engineering ◽  
Standard Support Vector Machine ◽  
Calculation Results

Inverse analysis is regarded as an ideal way to achieve the mechanical parameters of rock mass using in situ measured deformation data of surrounding rock during the construction of underground engineering. Aiming at the disadvantage of high computational complexity when identifying mechanical parameters of surrounding rock by employing the inverse method based on standard support vector machine (Vapnik’s SVM), a new back analysis method based on least squares support vector machine (LS-SVM) was presented. The basic principle of the method was introduced. An example was adopted to investigate the practicality and reliability of the method, and the calculation results indicated that, compared with the inversion method based on standard SVM, the method proposed in this paper possesses higher calculation efficiency and inversion precision.

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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