LS-SVM

AcronymDefinition
LS-SVMLeast Square Support Vector Machine
References in periodicals archive ?
Therefore, the LS-SVM, which has strong fitting ability to complex nonlinear problems, is used to establish the forecasting model for each high frequency component, and obtain each prediction value and sum each prediction value up to obtain the final high frequency prediction value [y.sub.1](t);
Improved Wind Speed Prediction Using Empirical Mode Decomposition
Then, LS-SVM regression estimate using kernel function K(x, [x.sub.i]) can be expressed by formula (10).
Error Compensation Technique for a Resistance-Type Differential Pressure Flow Sensor
By using the least squares support vector machine (LS-SVM) as the classifier, they obtained an overall accuracy of 89.66%, which is a bit lower than an accuracy of 93.10% obtained in the present study.
Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System
Feature Work set Classifiers Effectiveness [8] A Optimized SVM Accuracy = 99.2% Sensitivity = 98.43% Specificity = 100% [66] B NN Accuracy = 88.4% [10] A KNN Accuracy = 96.8% Sensitivity = 100% Specificity = 93.7% [9] A LS-SVM Accuracy = 99.7% Sensitivity = 99.6% Specificity = 99.8% [27] A SVM Accuracy = 79.71% [7] A LS-SVM Accuracy = 100% [19] B Fuzzy rule Accuracy = 84% Sensitivity = 79% Specificity = 89% [32] B Fuzzy rule Accuracy = 92.8% [58] B Fuzzy rule Accuracy = 81.2% [67] B Fuzzy rule and Accuracy = 84.44% ensemble classifier [55] A Random forest Sensitivity = 80% Specificity = 90% [44] A SVM with RBF Sensitivity = 73% Specificity = 87% [45] A SVM Sensitivity = 85% Specificity = 78%
Automated Diagnosis of Coronary Artery Disease: A Review and Workflow
The remaining part of this paper is organized as follows: Section 2 describes power system modeling, Section 3 contains the mathematical framework of the proposed TSI along with the model of the LS-SVM. In Section 4, simulation and results are presented, while Section 5 highlights major conclusions drawn from the work and also presents a solid milieu for future work.
Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine
Keywords: Wind forecast, WRF/YSU/Noah, BP-ANN, LS-SVM
Prediction of hub height winds over the plateau terrain by using WRF /YSU/Noah and statistical forecast/Prediccion de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la prediccion estadistica
The three-layer back propagation artificial neural network (BP-ANN) and LS-SVM are adopted, which have been applied successfully in downscaling temperature [12].
Statistical Downscaling of Temperature with the Random Forest Model
Deng, "Aeroengine abrasive identification based on IGA and LS-SVM," Lubrication, vol.
Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine
To compare the influences of different data preprocessing methods on calibration models, all the LS-SVM models are trained and validated using the same training and test sets.
Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process
In order to further validate the effectiveness of attribute reduction based on consistent covering rough set, the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) [29] and Relevance Vector Machine (RVM) [30].
Attribute Reduction Based on Consistent Covering Rough Set and Its Application
In order to reduce computing time and enhance recognition accuracy, the least squares SVM (LS-SVM) was proposed.
A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction
Usage of Least Square Support Vector Machine (LS-SVM)
Remote diagnosis, maintenance and prognosis for advanced driver assistance systems using machine learning algorithms
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