Searching for Weighted Least Squares Support Vector Machines information? Find all needed info by using official links provided below.
https://www.sciencedirect.com/science/article/pii/S0925231201006440
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem.Cited by: 1444
https://www.sciencedirect.com/science/article/abs/pii/S0925231201006440
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem.Cited by: 1444
https://www.iis.sinica.edu.tw/page/jise/2014/201411_06.pdf
Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM.
https://core.ac.uk/download/pdf/34266169.pdf
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and
https://www.researchgate.net/publication/220552601_Weighted_Least_Squares_Support_Vector_Machines_robustness_and_sparse_approximation
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution...
https://www.sciencedirect.com/science/article/abs/pii/S0925231215001708
Recently proposed least squares projection twin support vector machine (LSPTSVM), as a variant of projection twin support vector machine (PTSVM), attempts to further enhance the performance of PTSVM, whose solution follows from solving two sets of linear equations whereas PTSVM needs to solve two quadratic programming problems along with two sets of linear equations.Cited by: 19
https://www.sciencedirect.com/science/article/pii/S0925231201006440
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem.Cited by: 1444
https://www.sciencedirect.com/science/article/abs/pii/S0925231201006440
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem.Cited by: 1444
https://www.researchgate.net/publication/220552601_Weighted_Least_Squares_Support_Vector_Machines_robustness_and_sparse_approximation
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution...
https://www.iis.sinica.edu.tw/page/jise/2014/201411_06.pdf
Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM.
https://core.ac.uk/download/pdf/34266169.pdf
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and
https://www.sciencedirect.com/science/article/abs/pii/S0925231215001708
Recently proposed least squares projection twin support vector machine (LSPTSVM), as a variant of projection twin support vector machine (PTSVM), attempts to further enhance the performance of PTSVM, whose solution follows from solving two sets of linear equations whereas PTSVM needs to solve two quadratic programming problems along with two sets of linear equations.Cited by: 19
https://www.researchgate.net/publication/224132642_Weighted_least_squares_twin_support_vector_machines_for_pattern_classification
Widely weighted least squares twin support vector machine (WWLSTSVM) is proposed for pattern classification. In WWLSTSVM, weights are widely added on error variables of …
https://www.sciencedirect.com/science/article/pii/S0925231215001708
Recently proposed least squares projection twin support vector machine (LSPTSVM), as a variant of projection twin support vector machine (PTSVM), attempts to further enhance the performance of PTSVM, whose solution follows from solving two sets of linear equations whereas PTSVM needs to solve two quadratic programming problems along with two sets of linear equations.Cited by: 19
https://www.worldscientific.com/worldscibooks/10.1142/5089
Nov 01, 2002 · This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory.
https://core.ac.uk/download/pdf/34266169.pdf
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and
https://link.springer.com/article/10.1007/s11277-018-5478-y
The recently proposed semi-supervised least squares support vector machine (SLS-SVM), extends support vector machine (SVM) to semi-supervised learning field. However, the support value in SLS-SVM is not zero and the solution is lack of sparseness.
https://www.sciencedirect.com/science/article/pii/S0925231214004718
Weighted least squares support vector machines (WLS-SVMs) can partly overcome this shortcoming by assigning different weights to different training samples. However, it is a difficult task for WLS-SVMs to set the weights of the training samples, which greatly influences the robustness of WLS-SVMs.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.7566
Least Squares Support Vector Machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way the solution follows from a linear Karush-Kuhn-Tucker system instead of a quadratic programming problem.
https://ieeexplore.ieee.org/document/5451483/
Feb 28, 2010 · In this paper we propose a weighted version of recently developed least squares twin support vector machine (LSTSVM) for pattern classification, in which d Weighted least squares twin support vector machines for pattern classification - IEEE Conference Publication
https://dl.acm.org/doi/10.5555/2779626.2779767
Recently proposed least squares projection twin support vector machine (LSPTSVM), as a variant of projection twin support vector machine (PTSVM), attempts to further enhance the performance of PTSVM, whose solution follows from solving two sets of linear equations whereas PTSVM needs to solve two quadratic programming problems along with two sets of linear equations.
https://link.springer.com/article/10.1007/s11063-016-9527-9
May 12, 2016 · In particular, for the least squares case, the intractable computational burden may be incurred for large scale data. To address the above problems, we propose the double-weighted least squares twin bounded support vector machines and develop the online learning algorithms.
https://www.researchgate.net/publication/220578095_Least_Squares_Support_Vector_Machine_Classifiers
In least squares support vector machines (LS-SVMs) for function estimation Vapnik's ε-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression.
https://www.researchgate.net/publication/3839275_Multiclass_least_squares_support_vector_machines
We present an extension of least squares support vector machines (LS-SVMs) to the multiclass case. While standard SVM solutions involve solving quadratic or linear programming problems, the least ...
https://link.springer.com/article/10.1023%2FA%3A1018628609742
Abstract. In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
https://dl.acm.org/citation.cfm?id=326408
In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s.
https://www.ijser.org/paper/Brain-MR-Image-Classification-Using-Least-Squares-Support-Vector-Machine.html
The results of Least Squares Support Vector Machine are analysed to detect the tumour type. 3.1 Medical Image / MR Image ( Input Image Data) Magnetic Resonance Imaging (MRI) uses magnetic energy and radio waves to create images (“slices”) of the human body.
https://link.springer.com/chapter/10.1007/11539087_7
Abstract. Based on rolling optimization method and on-line learning strategies, a novel weighted least squares support vector machines (WLS-SVM) are proposed for nonlinear system identification in …
https://www.hindawi.com/journals/mpe/2013/602341/
The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM).
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