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How to create a support vector machine?

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Open access•Proceedings Article•DOI Using bag-of-concepts to improve the performance of support vector machines in text categorization Magnus Sahlgren, Rickard Cöster - Show less +1 more 23 Aug 2004 146 Citations	We also demonstrate how the performance of the Support Vector Machine can be improved by combining representations.
Open access•Journal Article•DOI Support Vector Machines for Classification in Nonstandard Situations Yi Lin, Yoonkyung Lee, Grace Wahba - Show less +2 more 11 Mar 2002-Machine Learning 385 Citations	Simulation study illustrates that the modified support vector machine significantly improves upon the standard support vector machine in the nonstandard situation.
Journal Article•DOI Support vector machine based on random set samples Chao Wang, Chao Wang, Minghu Ha, Minghu Ha - Show less +3 more 12 Aug 2013-International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems	Experimental results show that the new support vector machine is feasible and effective.
Proceedings Article•DOI Study on Classification Method Based on Support Vector Machine Hong Men, Yanchun Gao, Yujie Wu, Xiaoying Li - Show less +3 more 07 Mar 2009 9 Citations	The experimental results show that the support vector machine method is superior to the neural network algorithm.
Proceedings Article•DOI A support vector machines-based rejection technique for speech recognition Changxue Ma, M.A. Randolph, J. Drish - Show less +2 more 07 May 2001 62 Citations	We propose a new approach to training support vector machines.
Proceedings Article•DOI Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data Parisa Naraei, Abdolreza Abhari, Alireza Sadeghian - Show less +2 more 01 Dec 2016 47 Citations	Our results show that support vector machine is able to classify more accurately.
Proceedings Article•DOI Pre-extracting support vectors for support vector machine Zhang Li, Zhou Weida, Jiao Licheng - Show less +2 more 21 Aug 2000 34 Citations	Our experiment results show remarkable improvement of the speed of support vector machine, supporting our idea.
Open access•Journal Article•DOI Off-Line Handwritten Character Recognition System Using Support Vector Machine Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz - Show less +2 more 13 Dec 2017 13 Citations	It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

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What is a support vector machine?5 answersA support vector machine (SVM) is a powerful supervised learning model used for classification and regression tasks. It is based on statistical learning theory and convex optimization, making it a versatile tool in various domains such as bioinformatics, text categorization, and computer vision. SVMs aim to draw an optimal boundary in the covariate space to separate different classes or predict continuous outcomes. This technique maximizes the minimum distance between the samples and the boundary, even when the classes are not perfectly separable, by softening the margin concept. SVMs can handle tasks like pattern classification, regression estimation, and function approximation effectively, making them increasingly popular in machine learning applications. The method's nonlinear generalization through kernels allows for complex decision boundaries, enhancing its classification capabilities.

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 is the advantages of support vector machine?4 answersSupport Vector Machines (SVMs) have several advantages. Firstly, SVMs allow for the discovery of fewer model parameters and require less previous information and heuristic assumptions compared to earlier systems. Secondly, SVMs use quadratic programming for optimization, which is a well-researched and well-understood mathematical programming paradigm. Additionally, SVMs outperform neural networks on nonlinear issues studied, making them a superior choice for nonlinear detection. Furthermore, SVMs aim to find a decision surface that is maximally far away from any data point, maximizing classification accuracy, robustness, and generalization ability. SVMs are also known for their complete theory, global optimization, strong adaptability, and good generalization ability, making them a hot spot in machine learning research. Lastly, SVMs are nonparametric, flexible, and can easily handle large data sets with unknown, complex, and high-dimensional dependency structures, making them robust and suitable for various applications, including bioinformatics and genomics.

What is support vector machiences?4 answersSupport Vector Machines (SVM) is a machine learning algorithm used for regression and classification. It aims to find a decision surface that maximizes classification accuracy and robustness, and generalization ability. SVMs can be used for binary classification and linearly separable problems, as well as extended to nonlinearly separable problems and multiclass classification. SVMs have been applied in various fields such as pattern recognition and natural language processing. They use the principle of structural risk minimization to fit small data samples and employ the kernel trick to transform non-linear sample space into linear space, reducing algorithm complexity. SVMs have been shown to outperform neural networks in solving nonlinear issues. Overall, SVMs are a powerful tool in machine learning for classification tasks, offering the advantage of maximizing the margin between classes and providing robustness and generalization ability.

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.

How to create SVM model?8 answers

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