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What does SVM stand for Military?

Support vector machine

Quadratic programming

Particle swarm optimization

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Proceedings Article•DOI An Improved Particle Swarm Optimization for SVM Training Ying Li, Yan Tong, Bendu Bai, Yanning Zhang - Show less +3 more 24 Aug 2007 13 Citations	The experimental results show that the improved algorithm is feasible and effective for SVM training.
Open access•Journal Article•DOI A CSP\AM-BA-SVM Approach for Motor Imagery BCI System Sahar Selim, Manal M. Tantawi, Howida A. Shedeed, Amr Badr - Show less +3 more 31 Aug 2018-IEEE Access 78 Citations	The proposed CSP\AM-BA-SVM transcends the traditional CSP\SVM approach and other existing studies.
Proceedings Article•DOI Classification using support vector machines with graded resolution Lipo Wang, Bing Liu, Chunru Wan - Show less +2 more 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.
Proceedings Article•DOI Time Series Prediction Based on SVM and GA Wang Weiwei 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.
Proceedings Article•DOI Study of optimized SVM for incident prediction of a steel plant in India Sobhan Sarkar, Sammangi Vinay, Vishal Pateshwari, Jhareswar Maiti - Show less +3 more 01 Dec 2016 24 Citations	The results of experiments show that grid search-based SVM outperforms other optimized SVM approaches with 88.0% accuracy.

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How does the Support Vector Machine (SVM) algorithm work?3 answersThe Support Vector Machine (SVM) algorithm is a binary classifier that aims to maximize the margin between classes in the feature space. It was proposed by V.Vapnik in 1965 and has since been widely applied in various domains such as pattern recognition and natural language processing. SVM works by transforming non-linear sample spaces into linear spaces using the kernel trick, which reduces algorithm complexity. The learning strategy of SVM is based on structural risk minimization rather than empirical risk minimization, allowing it to fit small data samples effectively. The algorithm solves a convex quadratic programming problem to find the optimal linear classifier with the maximum margin. SVM has shown promising results in terms of classification performance and computational efficiency, making it a valuable tool in machine learning applications.

What is the svm?4 answersSupport Vector Machine (SVM) is a widely used classifier in bioinformatics and machine learning. It is a powerful classification tool that has been used in cancer genomics and biomedical signal classification. SVMs are binary linear classifiers that can be extended to non-linear data using kernels. They have been used for various applications such as document classification, word-sense disambiguation, and drug design. SVMs have different parameters that can influence their accuracy, and selecting good parameter values is important. There are also various techniques and strategies for solving SVMs, and researchers have proposed different problem formulations and solvers. SVMs have been implemented in libraries like LibSVM, which is widely used by researchers. The complexity of SVMs is O(n^3), and the time complexity can be affected by the size of the data.

What parameter is suited for SVM sentiment analysis?4 answersThe performance of SVM for sentiment analysis varies depending on the kernel function used. In the context of sentiment analysis, two commonly used kernel functions for SVM are Polynomial and Radial Basis Function (RBF). In general, the performance of the Polynomial kernel is slightly higher than that of the RBF kernel. However, recent studies have explored other variations of SVM, such as twin objective function SVMs, which have shown superior accuracy and stability compared to traditional SVMs. Additionally, the optimization of SVM parameters using techniques like the Shark smell optimizer algorithm can significantly improve the accuracy of the model. Therefore, the choice of the most suitable parameter for SVM sentiment analysis depends on the specific requirements and goals of the analysis.

How to build SVM model in R?7 answers

Is SVM a GLM?7 answers

What package is SVM in R?5 answers

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