Results 11 to 20 of about 326,273 (311)
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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Robust linear and support vector regression [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000 The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear and nonlinear support vector estimators.
Olvi L. Mangasarian, David R. Musicant
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Convex support vector regression [PDF]
European Journal of Operational ResearchNonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers.
Dai, Sheng +3 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.
Wei Chu, S. Sathiya Keerthi
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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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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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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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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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