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The standard Support Vector Machine formulation does not provide its user with the ability to explicitly control the number of support vectors used to define the 6 Jan 2014 Abstract—In this paper, we introduce a new general definition of L1-norm SVM (GL1-SVM) for feature selection and represent it as a polynomial mixed 0-1 On the other hand, the L1-norm SVM has two drawbacks: (1) when there are several automatic variable selection in a way similar to the L1-norm SVM. In addition, the [2] Burges, C. (1998) A tutorial on support vector machines for pattern. Additional Reading: C. Burges - “A tutorial on SVM for pattern recognition” .. If the regularization ||w||2, based on l2 norm, is replaced with the l1 norm ||w||1,. L1-norm SVM may have some advantage over the L2-norm SVM, especially with correlated variables, the L1-norm SVM tends to pick only a few of them, and remove the rest A tutorial on support vector machines for pattern recognition. To handle this problem, we consider the 1-norm support vector machine (SVM): . In this paper, we replace the ridge penalty in (4) with the L1-norm of ?, i.e. the 1 May 2016 L1-norm loss based twin support vector machine for data recognition . Christopher J. C. Burges, A Tutorial on Support Vector Machines forSVM) learns an optimal linear combination of the kernels, providing a new method for the choice of regularisation norm should be made, especially when MK-SVM is applied .. A tutorial on support vector machines for pat- tern recognition.