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The computational results reveal that GWO-SVM approach achieved better classification accuracy outperforms both GA-SVM and typical SVMs.
The newly developed GC-SVM classifier was a powerful predictor of OS and DFS.
Proceedings ArticleDOI
Lipo Wang, Bing Liu, Chunru Wan 
25 Jul 2005
33 Citations
Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.
This framework provides new perspectives on some established GLM algorithms derived from SLM ones and also suggests novel extensions for some other SLM algorithms.
Open accessProceedings ArticleDOI
14 Aug 2009
10 Citations
Experiments demonstrated that the integrated GA-SVM approach is superior compared to conventional SVM applications.
Proceedings ArticleDOI
22 Oct 2007
12 Citations
The simulation result shows that the multiple SVM achieve significant improvement in the generalization performance in comparison with the single SVM model.
The results also demonstrate that the GA-SVM algorithm achieves a better improvement than SVM.

Related Questions

When to use svm?5 answersSupport Vector Machines (SVM) can be used in various fields such as classification, weather forecasting, and nonlinear function approximation. SVM is effective in classifying seismic signals and identifying noise in seismograms. It can also be used to develop simplified verification models for radar systems, enabling the understanding of device operating principles and calibration algorithms. In weather forecasting, SVM has been successful in precipitation nowcasting based on radar images, particularly in short-term forecasting. For nonlinear classification in high-dimensional spaces, kernel SVM is commonly used, while linear SVM is popular in applications involving high-dimensional spaces. In regression problems, SVM is a popular machine learning model that solves nonlinear function approximation problems, with the least square of errors (LS-SVM) performing better in general.
SVM in GEE what is it and steps?5 answersSupport Vector Machine (SVM) is a widely used classification algorithm that uses a decision boundary to separate classes. It is commonly used in multi-classification problems and relies on a hyperplane to separate data points in a high-dimensional space. SVM has been applied in various fields, including gully erosion susceptibility mapping (GESM), detection of the gray zone in terminal welding, and feature selection in pattern classification. In GESM, SVM was used to develop a susceptibility map by selecting conditioning factors and assessing prediction accuracy. In terminal welding, SVM was applied to detect the gray zone and reduce misjudgments. In pattern classification, an improved feature selection method utilizing SVM and chaos particle swarm optimization was proposed. These applications highlight the versatility and effectiveness of SVM in different domains.
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