Results 271 to 280 of about 326,273 (311)

Robust support vector regression in the primal

Neural Networks, 2008
The classical support vector regressions (SVRs) are constructed based on convex loss functions. Since non-convex loss functions to a certain extent own superiority to convex ones in generalization performance and robustness, we propose a non-convex loss function for SVR, and then the concave-convex procedure is utilized to transform the non-convex ...
Yongping Zhao, Jianguo Sun
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Robust truncated support vector regression

Expert Systems with Applications, 2010
In this paper, we utilize two @e-insensitive loss functions to construct a non-convex loss function. Based on this non-convex loss function, a robust truncated support vector regression (TSVR) is proposed. In order to solve the TSVR, the concave-convex procedure is used to circumvent this problem though transforming the non-convex problem to a sequence
Yong-Ping Zhao, Jian-Guo Sun
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Support Vector Regression for Surveillance Purposes

2006
This paper addresses the problem of applying powerful statistical pattern classification algorithm based on kernel functions to target tracking on surveillance systems. Rather than directly adapting a recognizer, we develop a localizer directly using the regression form of the Support Vector Machines (SVM).
Sedat Ozer, Hakan A. Çirpan, Nihat Kabaoglu   +2 more
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Smooth twin support vector regression

Neural Computing and Applications, 2010
Twin support vector regression (TSVR) was proposed recently as a novel regressor that tries to find a pair of nonparallel planes, i.e., e-insensitive up- and down-bounds, by solving two related SVM-type problems. However, it may incur suboptimal solution since its objective function is positive semi-definite and the lack of complexity control. In order
Xiaobo Chen 0001, Jian Yang 0003, Jun Liang 0004, Qiaolin Ye   +3 more
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On pairing Huber support vector regression

Applied Soft Computing, 2020
Abstract In this paper, a novel and efficient pairing support vector regression learning method using e − insensitive Huber loss function (PHSVR) is proposed where the e − insensitive zone having flexible shape is determined by tightly fitting the training samples.
S. Balasundaram, Subhash Chandra Prasad
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Robust regression with extreme support vectors

Pattern Recognition Letters, 2014
Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has
Wentao Zhu, Jun Miao, Laiyun Qing
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Support vector regression for classifier prediction

Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007
In this paper we introduce XCSF with support vector prediction:the problem of learning the prediction function is solved as a support vector regression problem and each classifier exploits a Support Vector Machine to compute the prediction. In XCSF with support vector prediction, XCSFsvm, the genetic algorithm adapts classifier conditions, classifier ...
LANZI, PIER LUCA, LOIACONO, DANIELE, A. Marelli   +2 more
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Balanced Support Vector Regression

2015
We propose a novel idea of regression - balancing the distances from a regression function to all examples. We created a method, called balanced support vector regression (balanced SVR) in which we incorporated this idea to support vector regression (SVR) by adding an equality constraint to the SVR optimization problem.
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Indefinite Support Vector Regression

2017
Non-metric proximity measures got wide interest in various domains such as life sciences, robotics and image processing. The majority of learning algorithms for these data are focusing on classification problems. Here we derive a regression algorithm for indefinite data representations based on the support vector machine.
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A geometric approach to support vector regression

Neurocomputing, 2003
We develop an intuitive geometric framework for support vector regression (SVR). By examining when †-tubes exist, we show that SVR can be regarded as a classification problem in the dual space. Hard and soft †-tubes are constructed by separating the convex or reduced convex hulls respectively of the training data with the response variable shifted up ...
Jinbo Bi, Kristin P. Bennett
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