Results 11 to 20 of about 875,908 (286)
A tutorial on support vector regression [PDF]
Statistics and Computing, 2004 In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets.
Smola, Alexander, Schoelkopf, Bernhard
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Incremental Support Vector Learning for Ordinal Regression
IEEE Transactions on Neural Networks and Learning Systems, 2015 Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR.
Gu, Bin +4 more
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Support Vector Ordinal Regression [PDF]
Neural Computation, 2007 In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
Chu, Wei, Keerthi, S. Sathiya
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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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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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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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