Results 261 to 270 of about 326,273 (311)
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Rough support vector regression
European Journal of Operational Research, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pawan Lingras, Cory J. Butz
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Support Vector Regression for GDOP
2008 Eighth International Conference on Intelligent Systems Design and Applications, 2008Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organized geometrically. The calculation of GDOP is a time- and power-consuming task which can be done by solving measurement equations with complicated matrix transformation and inversion. This paper presents a support vector regression (SVR)
Wei-Han Su, Chih-Hung Wu
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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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On Lagrangian support vector regression
Expert Systems with Applications, 2010Prediction by regression is an important method of solution for forecasting. In this paper an iterative Lagrangian support vector machine algorithm for regression problems has been proposed. The method has the advantage that its solution is obtained by taking the inverse of a matrix of order equals to the number of input samples at the beginning of the
S. Balasundaram, Kapil 0001
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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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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
Junshui Ma, James Theiler, Simon Perkins
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Analysis of Support Vector Machines Regression
Foundations of Computational Mathematics, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongzhi Tong, Di-Rong Chen, Lizhong Peng
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A Note on Octonionic Support Vector Regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited.
Alistair Shilton +3 more
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Field Support Vector Regression
2017In regression tasks for static data, existing methods often assume that they were generated from an identical and independent distribution (i.i.d.). However, violation can be found when input samples may form groups, each affected by a certain different domain.
Haochuan Jiang +2 more
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Multi-scale Support Vector Regression
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010A multi-kernel Support Vector Machine model, called Hierarchical Support Vector Regression (HSVR), is proposed here. This is a self-organizing (by growing) multiscale version of a Support Vector Regression (SVR) model. It is constituted of hierarchical layers, each containing a standard SVR with Gaussian kernel, at decreasing scales.
S. Ferrari +3 more
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