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Accurate On-line Support Vector Regression
Neural Computation, 2003Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector
Ma, Junshui +2 more
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MBSVR: Multiple birth support vector regression
Information Sciences, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Zichen, Ding, Shifei, Sun, Yuting
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Rough support vector regression
European Journal of Operational Research, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lingras, P., Butz, C. J.
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Complex support vector regression
2012 3rd International Workshop on Cognitive Information Processing (CIP), 2012We present a support vector regression (SVR) rationale for treating complex data, exploiting the notions of widely linear estimation and pure complex kernels. To compute the Lagrangian and derive the dual problem, we employ the recently presented Wirtinger's calculus on complex RKHS.
Pantelis Bouboulis +2 more
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Fuzzy support vector regression
2011 1st International eConference on Computer and Knowledge Engineering (ICCKE), 2011The epsilon-SVR has two limitations. Firstly, the tube radius (epsilon) or noise rate along the -axis must be already specified. Secondly, this method is suitable for function estimation according to training data in which noise is independent of input (is constant).
Yahya Forghani +3 more
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Relaxed support vector regression
Annals of Operations Research, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Orestis P. Panagopoulos +3 more
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Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing, 2007A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi ...
Karl S, Ni, Truong Q, Nguyen
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Analysis of Support Vector Machines Regression
Foundations of Computational Mathematics, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tong, Hongzhi +2 more
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Automated support vector regression
Journal of Chemometrics, 2016Multivariate calibration is an important procedure for analytical chemistry. Automated or self‐configuring methods can be used by scientists who lack expertise, may be embedded into data processing pipelines, and are less prone to user bias; however, the development of such algorithms is often neglected by the chemometrics community.
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Block-Quantized Support Vector Ordinal Regression
IEEE Transactions on Neural Networks, 2009Support vector ordinal regression (SVOR) is a recently proposed ordinal regression (OR) algorithm. Despite its theoretical and empirical success, the method has one major bottleneck, which is the high computational complexity. In this brief, we propose a both practical and theoretical guaranteed algorithm, block-quantized support vector ordinal ...
Bin, Zhao, Fei, Wang, Changshui, Zhang
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