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https://www.sciencedirect.com/science/article/pii/S0893608009002019
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated.This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x.Like the previous v …Cited by: 97
https://www.researchgate.net/publication/26776492_New_support_vector_algorithms_with_parametric_insensitivemargin_model
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an …
https://dl.acm.org/citation.cfm?id=1660541
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x.Cited by: 97
https://www.sciencedirect.com/science/article/abs/pii/S0893608009002019
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated.This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x.Like the previous v …Cited by: 97
https://dl.acm.org/doi/10.1016/j.neunet.2009.08.001
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an …
http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf
New Support Vector Algorithms 1209 Figure 1: In SV regression, a desired accuracyeis speci” ed a priori. It is then attempted to ” t a tube with radiuseto the data. The trade-off between model complexity andpoints lying outside the tube (withpositive slackvariablesj)is determined by minimizing the expression 1.5. subjectto ((w¢xi)Cb)¡yi ...
https://www.sciencedirect.com/journal/neural-networks/vol/23/issue/1
Read the latest articles of Neural Networks at ScienceDirect.com, Elsevier’s leading platform of peer-reviewed scholarly literature
https://www.researchgate.net/publication/4375709_A_New_Support_Vector_Classification_Algorithm_with_Parametric-Margin_Model
In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius.
https://www.researchgate.net/publication/302915730_A_Fast_Bounded_Parametric_Margin_Model_for_Support_Vector_Machine
In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an …
https://www.researchgate.net/publication/282626853_A_novel_parametric-insensitive_nonparallel_support_vector_machine_for_regression
Request PDF A novel parametric-insensitive nonparallel support vector machine for regression In this paper, a novel parametric-insensitive nonparallel support vector regression (PINSVR ...
https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs1743
3 Least squares support vector machine with parametric margin (Par-LSSVM). In this section, we propose the least squares version of SVM with parametric margin for binary classification problem in details. Our Par-LSSVM is a fast and simple algorithm and only needs to solve a system of linear equations for generating both linear and nonlinear classifiers.
https://link.springer.com/article/10.1007/s10489-013-0423-y
In this paper, we propose a novel least squares twin parametric-margin support vector machine (TPMSVM) for binary classification, called LSTPMSVM for …
https://link.springer.com/article/10.1007/s10479-017-2724-8
Dec 18, 2017 · In this paper we introduce a new idea that converts this problem into an unconstrained convex problem. Moreover, we propose an extension of Newton’s method for solving the unconstrained convex problem. We compare the accuracy and efficiency of our method with support vector machines and parametric \(\nu \)-support vector regression methods ...
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004633826
In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 les v les 1 on ...
https://papers.nips.cc/paper/1563-shrinking-the-tube-a-new-support-vector-regression-algorithm.pdf
over, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally. 1 INTRODUCTION Support Vector (SV) machines comprise a new class of learning algorithms, motivated by results of statistical learning theory (Vapnik, 1995). Originally developed for pattern recog
https://www.sciencedirect.com/science/article/pii/S0925231215009455
In this paper, a novel parametric-insensitive nonparallel support vector regression (PINSVR) algorithm for data regression is proposed. PINSVR indirectly finds a pair of nonparallel proximal functions with a pair of different parametric-insensitive nonparallel proximal functions by solving two smaller sized quadratic programming problems (QPPs).
https://www.researchgate.net/publication/282626853_A_novel_parametric-insensitive_nonparallel_support_vector_machine_for_regression
Request PDF A novel parametric-insensitive nonparallel support vector machine for regression In this paper, a novel parametric-insensitive nonparallel support vector regression (PINSVR ...
https://machinelearningmastery.com/parametric-and-nonparametric-machine-learning-algorithms/
Support Vector Machines; Benefits of Nonparametric Machine Learning Algorithms: ... Non-parametric models do not need to keep the whole dataset around, but one example of a non-parametric algorithm is kNN that does keep the whole dataset. Instead, non-parametric models can vary the number of parameters, like the number of nodes in a decision ...
https://www.sciencedirect.com/topics/neuroscience/support-vector-machines
Support Vector Machines ... they can also be extended to solve nonlinear regression problems by the introduction of ε-insensitive loss function. In support vector regression, ... which is referred to as the margin, is maximal. New examples are then classified on the basis of their position relative to the hyperplane. Those that fall “above ...
https://dl.acm.org/citation.cfm?id=2839886
In this paper, a novel parametric-insensitive nonparallel support vector regression (PINSVR) algorithm for data regression is proposed. PINSVR indirectly finds a pair of nonparallel proximal functions with a pair of different parametric-insensitive nonparallel ...
https://www.ijcaonline.org/archives/volume77/number14/13554-1367
Qing Tao, Dejun Chu, Jue Wang, "Recursive Support Vector Machines for Dimensionality Reduction" IEEE Transactions on Neural Networks, Vol. 19, No. 1, January 2008. Pei-Yi Hao, "New support vector algorithms with parametric insensitive/margin model", Neural Networks 23 (2010) 60-73.
https://www.sciencedirect.com/science/article/pii/S0030402613012515
Real estate price forecasting based on SVM optimized by PSO. ... nonlinear, small samples), a new model based on support vector machine(SVM), which is aiming at the existing problems in the above methods, was proposed in this paper for real estate price forecasting. ... P.Y. HaoNew support vector algorithms with parametric insensitive/margin model.
https://dl.acm.org/citation.cfm?id=2589520
Least squares twin parametric-margin support vector machine for classification. Authors: Yuan-Hai Shao: ... A novel twin parametric-margin support vector machine for pattern recognition, Pattern Recognition, v.44 n.10-11, ... New support vector algorithms with parametric insensitive/margin model, Neural Networks, v.23 n.1, ...
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