Slope Deformation Prediction Based on Support Vector Machine

This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM). To explore the prediction process, phase space is reconstructed. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

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Dam Deformation Prediction Based on Modified Grey Wolf Optimization Algorithm and Support Vector Machine

Journal of Physics Conference Series ◽

10.1088/1742-6596/2005/1/012084 ◽

2021 ◽

Vol 2005 (1) ◽

pp. 012084

Author(s):

Li Mingjun ◽

Wang junxing ◽

Pan Jiangyang ◽

Peng Cheng ◽

Li Songzhang

Keyword(s):

Support Vector Machine ◽

Optimization Algorithm ◽

Support Vector ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Deformation Prediction ◽

Dam Deformation

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Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

Periodica Polytechnica Civil Engineering ◽

10.3311/ppci.7732 ◽

2016 ◽

pp. 361-369 ◽

Cited By ~ 1

Author(s):

Ma Cong ◽

◽

Tan Yuehu ◽

Li Erbing ◽

Yang Meng ◽

...

Keyword(s):

Support Vector Machine ◽

Surrounding Rock ◽

Support Vector ◽

Deformation Prediction ◽

Underground Caverns

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Deformation Prediction of Deep Excavation Using Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.66 ◽

2012 ◽

Vol 157-158 ◽

pp. 66-69 ◽

Cited By ~ 1

Author(s):

Xu Chao Shi ◽

Wu Xin Chen ◽

Xiu Juan Lv

Keyword(s):

Pattern Recognition ◽

Support Vector Machine ◽

Support Vector Machine Model ◽

Support Vector ◽

Prediction Problem ◽

Deep Excavation ◽

Comparison Analysis ◽

Recognition Method ◽

Machine Model ◽

Deformation Prediction

Support Vector Machine (SVM) is a new pattern recognition method developed in recent years on the foundation of statistical learning theory. It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. Due to the complexity of the deep excavation, deformation prediction problem has not been a good solution. In the paper the support vector machine model was proposed to predict the deep excavation deformation. On the basis of deep excavation displacement data measured with real time series, the model of deep excavation displacement with time was built by SVM. Typical deformation data of deep excavation is used as learning and test samples. Comparison analysis is made between calculated values generated by SVM method and observed values. The result shows this method is feasible and effective.

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

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.

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