Slope Displacement Forecasting of the Substations Based on the Support Vector Machine and Environmental Factor

2012 ◽

Vol 594-597 ◽

pp. 2932-2935

Author(s):

Fei Xu ◽

Ke Wang

Keyword(s):

Support Vector Machine ◽

Hydroelectric Station ◽

Least Square ◽

Support Vector ◽

Time Series Forecast ◽

Chaos Optimization ◽

Wavelet Kernel Function ◽

Optimized Model ◽

Optimum Solution ◽

Slope Displacement

A multi-dimension admissible support vector wavelet kernel function is introduced and the model of wavelet least square support vector machine (WLSSVM) is optimized by chaos optimization (CO), which is named as wavelet least squares support vector machine based on chaos optimization (CO-WLSSVM).The optimized model improves the forecasting precision depending multi-dimension interpolation character and sparse change character of the wavelet and quick convergence to the optimum solution of the chaos optimization. The CO-WLSSVM is applied to forecast the displacement of left side bank of slope in first-stage hydroelectric station of Jinping. The result shows that the model possesses higher precision of forecasting.

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Slope Deformation Prediction Based on Chaotic-SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.353-356.673 ◽

2013 ◽

Vol 353-356 ◽

pp. 673-677

Author(s):

Lei Jia ◽

Yuan Li ◽

Yong Ping Xie

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Slope Deformation ◽

Support Vector ◽

Single Variable ◽

Deformation Prediction ◽

Slope Engineering ◽

Training Samples ◽

Slope Displacement ◽

Fitting Accuracy

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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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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