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Possibilistic support vector machines
Pattern Recognition, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ki Young Lee +3 more
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IEEE Transactions on Neural Networks, 2002
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
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A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
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Optimisation on support vector machines
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000We deal with the optimisation problem involved in determining the maximal margin separation hyperplane in support vector machines. We consider three different formulations, based on L/sub 2/ norm distance (the standard case), L/sub 1/ norm, and L/sub /spl infin// norm.
João Pedro Pedroso, Noboru Murata
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Functional support vector machine
BiostatisticsAbstract Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the ...
Shanghong, Xie, R Todd, Ogden
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On Consistency and Stability of Support Vector Machines and Localized Support Vector Machines
In recent years, the demand for machine learning and artificial intelligence has grown rapidly. This has manifested itself in a drastic increase in the number of existing applications as well as in the pervasiveness of these applications. In these, different machine learning methods have shown enormous empirical success in accurately capturing ...openaire +2 more sources
Lagrangian support vector machines
J. Mach. Learn. Res., 2001Summary: An implicit Lagrangian for the dual of a simple reformulation of the standard quadratic program of a linear support vector machine is proposed. This leads to the minimization of an unconstrained differentiable convex function in a space of dimensionality equal to the number of classified points.
Olvi L. Mangasarian, David R. Musicant
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Sparseness of support vector machines
J. Mach. Learn. Res., 2003Summary: Support Vector Machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors.
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Support vector machine interpretation
Neurocomputing, 2006Ángel Navia-Vázquez +1 more
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LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology, 2011Chih-Jen Lin, Chih-Chung Chang
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