Results 11 to 20 of about 869,216 (289)
Regression depth and support vector machine [PDF]
, 2006 The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression.
Christmann, Andreas
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Distributed Support Vector Ordinal Regression over Networks
Entropy, 2022 Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization.
Huan Liu, Jiankai Tu, Chunguang Li
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Support Vector Regression with Interval-Input Interval-Output [PDF]
International Journal of Computational Intelligence Systems, 2008 Support vector machines (classification and regression) are powerful machine learning techniques for crisp data. In this paper, the problem is considered for interval data. Two methods to deal with the problem using support vector regression are proposed
Wensen An, Cecilio Angulo, Yanguang Sun
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Incremental Reduced Lagrangian Asymmetric ν-Twin Support Vector Regression [PDF]
Jisuanji kexue yu tansuo, 2023 Lagrangian asymmetric ν-twin support vector regression is a prediction algorithm with good generalization performance. However, it is unsuitable for the scenarios where the samples are provided incrementally.
ZHANG Shuaixin, GU Binjie, PAN Feng
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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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Support vector regression model with variant tolerance
Measurement + Control, 2023 Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius ε -tube, affording good predictive performance on datasets.
Jiangyue Wei, Xiaoxia He
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BSP-Based Support Vector Regression Machine Parallel Framework [PDF]
International Journal of Networked and Distributed Computing (IJNDC), 2013 In this paper, we investigate the distributed parallel Support Vector Machine training strategy, and then propose a BSP-Based Support Vector Regression Machine Parallel Framework which can implement the most of distributed Support Vector Regression ...
Hong Zhang, Yongmei Lei
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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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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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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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