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What package is SVM in R?

Genetic algorithm

Support vector machine

Feature (machine learning)

Structured support vector machine

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Proceedings Article•DOI A combined SVM and LDA approach for classification Tao Xiong, Vladimir Cherkassky - Show less +1 more 27 Dec 2005 1K Citations	We show that existing SVM software can be used to solve the SVM/LDA formulation.
Journal Article•DOI Support Vector Machine Classification Trees Peter de B. Harrington 22 Oct 2015-Analytical Chemistry 30 Citations	The novel feature is that the distribution of the data objects is used to determine the SVM encoding.
Open access•Journal Article•DOI Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine. Niloofar Yousefi Moteghaed, Keivan Maghooli, Masoud Garshasbi - Show less +2 more 07 Jan 2018-Journal of medical signals and sensors 10 Citations	The results show that the best kernel used in training the SVM classifier is the radial basis function.
Open access•Book Chapter•DOI A Comparison of Multiclass SVM Methods for Real World Natural Scenes Can Demirkesen, Hocine Cherifi - Show less +1 more 20 Oct 2008 40 Citations	Our experimental results show that all-in-one SVM outperforms the other methods.
Open access•Proceedings Article•DOI GA-SVM Based Framework for Time Series Forecasting Thi H. O. Nguyen, Lee Gordon-Brown, Peter Wheeler, James Andrew Peterson - Show less +3 more 14 Aug 2009 10 Citations	Experiments demonstrated that the integrated GA-SVM approach is superior compared to conventional SVM applications.

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SVM (Support Vector Machines) ?5 answersSupport Vector Machine (SVM) is a powerful machine learning algorithm primarily used for classification tasks. SVM works by finding the optimal decision boundary that maximizes the margin between different classes in the data, aiming to enhance classification accuracy, robustness, and generalization ability. It is widely applied in various fields, including drug design, where SVM aids in optimizing chemical structures, ensuring drug safety, target discovery, and even in COVID-19-related research. SVM can handle both linearly and nonlinearly separable problems, making it versatile for different types of datasets. Additionally, SVM can be adapted for regression tasks, showcasing its flexibility in addressing a wide range of machine learning challenges.

What are the application of SVM?3 answersSupport Vector Machines (SVMs) have a wide range of applications in various fields. They are used for classification and regression tasks in pattern recognition and have gained recognition in scientific and engineering areas. SVMs have been applied in text classification, image characterization, protein function prediction, and data classification. In the field of drug discovery, SVMs are used for substance classification, regression modeling, and virtual screening of compounds. SVMs have also been used in intrusion detection systems to efficiently detect abnormal intrusions. Additionally, SVMs have been extensively researched in data mining and machine learning communities and have been applied in various domains for classification, regression, and ranking functions.

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