Support vector regressionNonparallel support vector machinesIn this work, a novel method called epsilon-nonparallel support vector regression (蔚-NPSVR) is proposed. The reasoning behind the nonparallel support
Support vector regressionNonparallel support vector machinesIn this work, a novel method called epsilon-nonparallel support vector regression (蔚-NPSVR) is proposed. The reasoning behind the nonparallel support vector machine (NPSVM) method for binary classification is extended for predicting numerical ...
Kwok J T,Tsang I W.Linear dependency between epsilon and the input noise in epsilon-support vector regression. IEEE Transactions on Neural Networks . 2003J. T. Kwok and I. W. Tsang, "Linear Dependency Between ε and the Input Noise in ε-Support Vector Regression," IEEE Trans. Neural ...
On this basis, the damage degree prediction model of CFRP structure based on epsilon-support vector regression was established. Finally, the method proposed in this paper was experimentally verified. The results showed that the epsilon-support vector regression model can accurately predict the damage ...
this study takes cubic spline interpolation to generate a new polynomial smooth function |x|_ε~2 in ε-insensitive support vector regression.Theoretical analysis shows that S_ε~2-function is better than p_ε~2-function in properties,and the approximation accuracy of the proposed smoothing ...
tillagemoldboard plowdraft forcesupport vector regressionlearning algorithmsThe draft force acting on a moldboard plow plays an important role in the design of more efficient plows to facilitate attainment of optimum results when implementing size matching to estimate the required tractor power. The ...
Linear Dependency Between \\\${extbackslash}epsilon$ and the Input Noise in {\\\${extbackslash}epsilon$-Support} Vector RegressionKwok, James TTsang, Ivor W
Identification of Linear Parameter-Varying Models with Unknown Parameter Dependence Using $varepsilon$ -Support Vector Regressiondoi:10.23919/acc.2018.8431736Adwait DatarErik SchulzHerbert WernerIEEEAdvances in Computing and Communications
Least Squares Support Vector Regression (LSSVR) which is a least squares version of the Support Vector Regression (SVR) is defined with a regularized squared loss without epsilon-insensitiveness. LSSVR is formulated in the dual space as a linear equality constrained quadratic minimizat...
Quantile regressionPinball loss functionSupport vector machineepsilon-insensitive loss functionIn this paper, we propose a novel asymmetric epsilon-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the epsilon-insensitive zone ...