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Support Vector Machines and Support Vector Regression [PDF]
, 2022 AbstractIn this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation
Osval Antonio Montesinos López +2 more
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Adaptive L0 Regularization for Sparse Support Vector Regression
Mathematics, 2023 In this work, we proposed a sparse version of the Support Vector Regression (SVR) algorithm that uses regularization to achieve sparsity in function estimation.
Antonis Christou, Andreas Artemiou
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Support vector regression for warranty claim forecasting [PDF]
, 2011 Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and ...
Akbarov, Artur +3 more
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On implicit Lagrangian twin support vector regression by Newton method [PDF]
International Journal of Computational Intelligence Systems, 2014 In this work, an implicit Lagrangian for the dual twin support vector regression is proposed. Our formulation leads to determining non-parallel –insensitive down- and up- bound functions for the unknown regressor by constructing two unconstrained ...
S. Balasundaram, Deepak Gupta
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Vector-Valued Support Vector Regression [PDF]
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006 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. A particular emphasis is placed on the class of loss functions chosen which apply the epsiv-insensitive loss ...
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Copper Price Prediction Using Support Vector Regression Technique
Applied Sciences, 2020 Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a ...
Gabriel Astudillo +3 more
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Incremental Reduced Least Squares Twin Support Vector Regression
Jisuanji kexue yu tansuo, 2021 In the incremental least squares twin support vector regression, to solve the problem that the constituted kernel matrix cannot approximate the original kernel matrix well, this paper proposes an incremental reduced least squares twin support vector ...
CAO Jie, GU Binjie, XIONG Weili, PAN Feng
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Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction
Revue des Énergies Renouvelables, 2022 At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the ...
Souhaila Chahboun, Mohamed Maaroufi
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Support vector regression model for flight demand forecasting
International Journal of Engineering Business Management, 2023 Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits.
Wei FAN +6 more
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Interpretable support vector regression
Artificial Intelligence Research, 2012 This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS). It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models. However, the transformed support vector model does not automatically result in an interpretable fuzzy model.
Tamás Kenesei, János Abonyi
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