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https://www.sciencedirect.com/science/article/pii/S0957417410005324
3. Lagrangian support vector regression algorithm. It was shown in the previous section that the dual problem for either the linear or nonlinear case can be formulated as (14) min 0 ⩽ u ∈ R 2 m 1 2 u t Qu-r t u, where u = u 1 u 2 is a vector in R 2m.Cited by: 20
https://www.sciencedirect.com/science/article/pii/S0925231216001284
Highlights • Weighted Lagrangian ε-twin support vector regression (WL-ε-TSVR) is proposed.. Weight matrix D is introduced to reduce the impact of outliers. • WL-ε-TSVR just needs to solve the simple unconstrained minimization problems (UMPs).A linearly convergent Lagrangian algorithm is used to obtain the solutions of UMPs.Cited by: 17
https://www.researchgate.net/publication/220214680_On_Lagrangian_support_vector_regression
Prediction by regression is an important method of solution for forecasting. In this paper an iterative Lagrangian support vector machine algorithm for regression problems has been proposed.
https://www.researchgate.net/publication/230752979_On_Lagrangian_twin_support_vector_regression
In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed.
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://dl.acm.org/doi/10.1016/j.neunet.2013.12.003
In this paper, a simple reformulation of the Lagrangian dual of the 2-norm support vector regression (SVR) is proposed as an unconstrained minimization problem. This formulation has the advantage t...
https://dl.acm.org/citation.cfm?id=2576318
In this paper, a simple reformulation of the Lagrangian dual of the 2-norm support vector regression (SVR) is proposed as an unconstrained minimization problem. This formulation has the advantage that its objective function is strongly convex and further having only …Cited by: 16
https://link.springer.com/article/10.1007/s00521-012-0971-9
Jun 21, 2012 · In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression …Cited by: 35
https://en.wikipedia.org/wiki/Least-squares_support-vector_machine
Least-squares support-vector machines (LS-SVM) are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.In this version one finds the solution by solving a set of linear equations instead of a convex quadratic programming (QP ...
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