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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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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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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Dirk Valkenborg +3 more
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Dirk Valkenborg +3 more
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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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Structural Support Vector Machine
2008Support Vector Machine (SVM) is one of the most popular classifiers in pattern recognition, which aims to find a hyperplane that can separate two classes of samples with the maximal margin. As a result, traditional SVM usually more focuses on the scatter between classes, but neglects the different data distributions within classes which are also vital ...
Hui Xue 0002 +2 more
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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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, 1999