Searching for 1 Norm Support Vector Machines Bibtex information? Find all needed info by using official links provided below.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.6182
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The standard 2-norm SVM is known for its good performance in twoclass classification. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.3564
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The standard 2-norm SVM is known for its good performance in two-class classification. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.9740
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an …
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5298
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Support vector machines utilizing the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstrained minimization of a convex differentiable piecewise-quadratic objective function in the dual space.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.578.7837
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Support vector machines utilizing the 1-norm, typically set up as linear programs (Man-gasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely un-constrained minimization of a convex differentiable piecewise-quadratic objective function in the dual space.
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.8761
BibTeX @TECHREPORT{Zhu03l1norm, author = {Ji Zhu and Saharon Rosset and Trevor Hastie and Rob Tibshirani}, title = {L1 norm support vector machines}, institution = {Advances in Neural Information Processing Systems}, year = {2003}}
https://arxiv.org/abs/1509.08083v1
Support Vector Machines (SVM) with $\\ell_1$ penalty became a standard tool in analysis of highdimensional classification problems with sparsity constraints in many applications including bioinformatics and signal processing. Although SVM have been studied intensively in the literature, this paper has to our knowledge first non-asymptotic results on the performance of $\\ell_1…Cited by: 1
https://dl.acm.org/citation.cfm?id=2981352
The standard 2-norm SVM is known for its good performance in two-class classification. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features.Cited by: 1000
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.631.8782
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract: In this paper we propose a new support vector machine (SVM), the F∞-norm SVM, to perform automatic factor selection in classification. The F∞-norm SVM methodology is motivated by the feature selection problem in cases where the input features are generated by factors, and the …
https://papers.nips.cc/paper/2450-1-norm-support-vector-machines.pdf
1-norm Support Vector Machines Ji Zhu, Saharon Rosset, Trevor Hastie, Rob Tibshirani Department of Statistics Stanford University Stanford, CA 94305 {jzhu,saharon,hastie,tibs}@stat.stanford.edu Abstract The standard 2-norm SVM is known for its good performance in two-class classi£cation. In this paper, we consider the 1-norm SVM. We
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