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https://www.sciencedirect.com/science/article/pii/S0047259X13001164
For each fixed triplet V ≔ (d, λ, K (d)) of hyper-parameters, the training set is used to calculate the solution obtained by Support Vector Machine Quantile Regression using the projected data Ψ ˆ d, λ, K (d) (x (d)) = ∑ j = 1 l α ˆ j K (d) (x (d), X j (d)), ∀ x (d) ∈ X (d), where α ˆ j ∈ R, 1 ≤ j …Cited by: 10
https://link.springer.com/chapter/10.1007%2F11539087_66
Abstract. This paper deals with the estimation of the linear and the nonlinear quantile regressions using the idea of support vector machine. Accordingly, the optimization problem is transformed into the Lagrangian dual problem, which is easier to solve.Cited by: 17
https://www.sciencedirect.com/science/article/pii/S0957417415001712
In contrast, we consider a weighted quantile regression via support vector machine (SVWQR). It is a general method that nests several well-known quantile regression methods, including the SVQR of Shim and Hwang (2010) , as special cases.Cited by: 16
https://www.researchgate.net/publication/221163687_A_Simple_Quantile_Regression_via_Support_Vector_Machine
Quantile regression can also be hybridized with machine learn- ing methods to form powerful nonlinear models. The idea of support vector regression is introduced for quantile regression model,...
https://www.sciencedirect.com/science/article/pii/S1568494619302534
Based on statistical learning theory, support vector machine (CSVM) is a powerful tool for pattern classification and regression tasks. With the hinge loss function for classification, CSVM enjoys good properties such as kernel skill, sparsity and global solutions.Cited by: 2
https://www.researchgate.net/publication/259475903_Support_Vector_Machine_Quantile_Regression_approach_for_Functional_Data_simulation_and_application_studies
The main goal is to establish a weak consistency of the Support Vector Machines estimator of conditional quantile under exponentially strongly mixing functional input sequences…
https://www.researchgate.net/publication/273912827_Weighted_quantile_regression_via_support_vector_machine
Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables.
https://arxiv.org/abs/1910.09168
Abstract: This paper proposes a novel '$\nu$-support vector quantile regression' ($\nu$-SVQR) model for the quantile estimation. It can facilitate the automatic control over accuracy by creating a suitable asymmetric $\epsilon$-insensitive zone according to the variance present in data.Author: Pritam Anand, Reshma Rastogi, Suresh Chandra
https://www.researchgate.net/publication/276088637_Semisupervised_support_vector_quantile_regression
Quantile regression is a basic tool for estimating conditional quantiles of a response variable Y given a vector of regressors X. It can be used to measure the effect of regressors not only in the...
https://link.springer.com/article/10.1007/s00180-016-0647-5
We propose a support vector quantile regression model with varying coefficients and its two estimation methods. One uses the quadratic programming, and the other uses the iteratively reweighted least squares procedure.
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