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, 2016 Support Vector Machine is one of the classical machine learning techniques that can still help solve big data classification problems. Especially, it can help the multidomain applications in a big data environment. However, the support vector machine is mathematically complex and computationally expensive.
Eihab Bashier Mohammed Bashier +2 more
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A multistage support vector machine
Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003The support vector machine (SVM) was originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. There exist several methods to construct a multiclass classifier by combing several binary classifiers, such as "one-against-one", "one-against-all" and directed acyclic graph
null Hong-Jie Xing +3 more
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2006
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples.
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Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples.
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2008
This chapter covers support vector machines (SVM). Support vector machines was developed as a classifier within computer science. They not an ensemble method but a special kind of margin maximizer that uses kernels as discussed in Chap. 2. Their look and feel is quite different form the procedures discussed in earlier chapters, but their performance ...
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This chapter covers support vector machines (SVM). Support vector machines was developed as a classifier within computer science. They not an ensemble method but a special kind of margin maximizer that uses kernels as discussed in Chap. 2. Their look and feel is quite different form the procedures discussed in earlier chapters, but their performance ...
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2019
This chapter covers another major and powerful machine learning tool which is SVM. The chapter begins with the introduction of linear classifier, K-NN classifier, and perceptron which are the key to understand discriminative approaches such as SVM and ANN. After these preparations, the primal form SVM is formally introduced.
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This chapter covers another major and powerful machine learning tool which is SVM. The chapter begins with the introduction of linear classifier, K-NN classifier, and perceptron which are the key to understand discriminative approaches such as SVM and ANN. After these preparations, the primal form SVM is formally introduced.
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ACM SIGAPL APL Quote Quad, 2004
This paper considers the implementation of Support Vector Machines (SVM), the new extensive class of data analysis methods. SVM have a number of advantages as compared with standard data mining techniques like artificial neural networks, for example. In the paper this methodology is described in details and implemented in A+ programming language in the
Alexander O. Skomorokhov +1 more
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This paper considers the implementation of Support Vector Machines (SVM), the new extensive class of data analysis methods. SVM have a number of advantages as compared with standard data mining techniques like artificial neural networks, for example. In the paper this methodology is described in details and implemented in A+ programming language in the
Alexander O. Skomorokhov +1 more
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2019
Three separate sections comprise this chapter. The first presents an overview of statistical learning theory (SLT) as applied to machine learning. The topics covered are empirical or true risk minimization, the risk minimization principle (RMP), theoretical concept of risk minimization, function f0(X) that minimizes the expected (or true) risk ...
J. David Schaffer, Walker H. LandJr.
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Three separate sections comprise this chapter. The first presents an overview of statistical learning theory (SLT) as applied to machine learning. The topics covered are empirical or true risk minimization, the risk minimization principle (RMP), theoretical concept of risk minimization, function f0(X) that minimizes the expected (or true) risk ...
J. David Schaffer, Walker H. LandJr.
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, 2004 Support Vector Machines (SVMs) are a relatively new generation of techniques for classification and regression problems. These are based on Statistical Learning Theory having its origins in Machine Learning, which is defined by Kohavi and Foster (1998) as, ...Machine Learning is the field of scientific study that concentrates on induction algorithms ...
Mahesh Pal, Pakorn Watanachaturaporn
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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Dirk Valkenborg +3 more
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Dirk Valkenborg +3 more
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2012
Support vector machines (SVMs) are supervised learning methods used for classification [30, 41, 232]. This is one of the techniques among the top 10 for data mining [237]. In their basic form, SVMs are used for classifying sets of samples into two disjoint classes, which are separated by a hyperplane defined in a suitable space.
Petros Xanthopoulos +3 more
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Support vector machines (SVMs) are supervised learning methods used for classification [30, 41, 232]. This is one of the techniques among the top 10 for data mining [237]. In their basic form, SVMs are used for classifying sets of samples into two disjoint classes, which are separated by a hyperplane defined in a suitable space.
Petros Xanthopoulos +3 more
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