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https://www.researchgate.net/publication/224654508_Vector-Valued_Support_Vector_Regression
A vector-valued extension of the support vector regression problem is presented here. The vector-valued variant is developed by extending the notions of the estimator, loss function and ...
https://ieeexplore.ieee.org/document/1716292/
Abstract: A vector-valued extension of the support vector regression problem is presented here. The vector-valued variant is developed by extending the notions of the estimator, loss function and regularization functional from the scalar-valued case.Cited by: 13
https://arxiv.org/pdf/1303.2184
Support Vector Regression as well as Support Vector Machines for quaternary classification. The method exploits the noti on of widely linear estimation to model the input-out relation for complex-valued data and considers two cases: a) the complexdata are split into their real and imaginary parts and a …Cited by: 25
https://www.sciencedirect.com/science/article/pii/S0020025513001102
In this paper, a new model named Multiclass Support Vector Machines with Vector-Valued Decision (M-SVMs-VVD) or VVD is proposed. The basic idea is to separate 2 a classes by a SVM hyperplanes in the feature space induced by certain kernels, where a is a finite positive integer. We start from a 2 a-class problem, and extend it to any-class problem by applying a hierarchical decomposition procedure.Cited by: 15
https://www.d.umn.edu/~rmaclin/publications/maclin-ijcai2005.pdf
Kernel Regression [Mangasarian et al., 2004]. 2.1 Support Vector Regression A linear regression problem involves trying to find a set of weights (w) and a offset (b) to learn a function of the form f(x) = wTx + be, where T indicates the transpose of a vector, x …
https://www.sciencedirect.com/science/article/pii/S0957417414005879
In this study, we address the regression problem on set-valued samples that appear in applications. To solve this problem, we propose a support vector regression approach for set-valued samples that generalizes the classical ε-support vector regression.First, an initial representative point (or an element) for every set-valued sample is selected, and a weighted distance between the initial ...Cited by: 8
http://bjll.org/index.php/jpm/article/viewFile/427/459
previous literature, a state-of-the-art modelling paradigm is developed which integrates least-square support vector regression and conditional logit procedures to predict horses’ winning probabilities. In order to adapt the least-square support vector regression model to this task, some free parameters
https://www.hindawi.com/journals/jcse/2017/3614790/
Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression.Cited by: 5
https://link.springer.com/chapter/10.1007/978-1-4302-5990-9_4
Apr 27, 2015 · The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems. As in classification, support vector regression (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors. Although less popular than SVM, SVR has been proven to be an ...Cited by: 31
https://www.researchgate.net/publication/228537532_Support_Vector_Regression
support vector mac hines: support ve ctor classification (SV C) and suppo rt vector regressio n (SVR). SVM is a learn ing system us ing a high dimen sional fea ture sp ace.
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