Wavelet kernel function based multiscale LSSVM for elliptic boundary value problems

TL;DR: Wang et al. as discussed by the authors proposed a multiprocess joint modeling method to establish the models of multiple industrial processes simultaneously under the assumption that the related processes shared common information, which views the process model as the combination of the common feature model and the special feature model.

TL;DR: In this paper, an HSI classification technique based on the weight wavelet kernel JSR ensemble model and the $\beta$ -whale optimization algorithm is proposed to conduct pixel-level classification, where the wavelet function is acted as the kernel of JSR.

TL;DR: The effective feasibility of the ABC-LSSVM combination method in the construction of GPS height fitting model is proved, which provides a certain reference value for the establishment of GPS Height fitting model.

TL;DR: Wang et al. as discussed by the authors proposed a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China, which improved the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, and 0.9231 in Guangdong.

TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.

TL;DR: Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS- sVM for Unsupervised Learning LS- SVM for Recurrent Networks and Control.

TL;DR: Meshless approximations based on moving least-squares, kernels, and partitions of unity are examined and it is shown that the three methods are in most cases identical except for the important fact that partitions ofunity enable p-adaptivity to be achieved.

TL;DR: A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions.