LS-SVM

AcronymDefinition
LS-SVMLeast Square Support Vector Machine
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The second section of this paper reviews related research works on estimating of construction cost at completion, LS-SVM, techniques for achieving prediction intervals, and DE.
In LS-SVM for regression analysis, given a training dataset [{[x.
The resulting LS-SVM model for function estimation is expressed as:
Finally, the regression model for LS-SVM can be obtained in the form of:
Construction of the Regression Model Based on LS-SVM
According to the basic principle of the LS-SVM regression problem, the regression process of SVM is shown in Fig.
In the model development using LS-SVM and radial basis function kernel, the optimal combination of gam ([gamma]) and [sig.
The principal components (PCs) obtained by PLS method were set as the optimal input variables of LS-SVM instead of original variables.
The PCs obtained by PLS method were set as the optimal input variables of LS-SVM instead of original variables.
c]); 1-nearest neighbor (1NN), 10-nearest neighbor (10NN), 100-nearest neighbor (100NN), 500-nearest neighbor (500NN); MLP having one output neuron and one hidden layer with up to three hidden layer neurons (MLP1,MLP2,MLP3); linear kernel LS-SVM (Lin-LS-SVM), polynomial kernel LS-SVM of degrees 2 and 3 (Poly2-LS-SVM, Poly3-LS-SVM), RBF kernel LS-SVM (RBF-LS-SVM); naive Bayes (NB), naive Bayes with Dirichlet smoothing confidence factor 5 ([NB.
The genetic algorithm is designed in the paper to solute the optimization of parameters in LS-SVM.
The procedure of LS-SVM parameter optimization based on the genetic algorithm is shown in Fig.