Konen. SVM ensembles are better when different kernel types are combined. In B. Lausen, editor, European Conference on Data Analysis (ECDA). (to appear), 2013.Stork J, Ramos R, Koch P, et al. SVM ensembles are better when different kernel types are combined[ M]//Data Science, Learning...
DWT2 has also been considered with three different types, namely, Haar wavelet, Daubechies (db) 1 wavelet and Daubechies (db) 2 wavelet transformations. In this work, we have used support vector machine (SVM) classifier for classification, as well as for linear kernel and polynomial kernel. ...
SVM kernel functions for classification[C]//Anon. International Conference on Advances in Technology and Engineering (ICATE). [S. 1. ]:[S. n. ],2013:1-9.. Google Scholar [18] ZHANG L,ZHANG B. Relationship between support vector set and kernel functions in SVM[J]. J. Comput. Sci...
22_ Kernel Density estimate In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite ...
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The classification of type, location, and extent of different defects is carried out using support vector machine with cubic polynomial kernel. The accuracy of different indices and four different components of TF are investigated. Moreover, a new index based on fitting performance is defined. The...
A non-parametric kernel density estimation method (NPKDE) is carried out in Ref. [61], in the domain of wind power forecasting. Similarly a gaussian-kernal based NPKDE and confidence intervals are used for modelling the error distribution of a solar power prediction model [62]. Confidence ...
Finally, the overall LSM accuracy of the RF is superior to that of the SVM, and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models. Keywords: Landslide boundary, Landslide susceptibility mapping, Machine learning, ...
the RBF kernel function is processed with higher accuracy than the polynomial and Sigmoid kernel functions, and has fewer parameters to be determined (penalization factor C, kernel parameter γ). For these reasons, RBF was chosen as the kernel function to build the SVM regression model. The same...
The LSP performance of the four machine learning models in the study region from high to low is RF, MLP, SVM, and NB. In addition, the overlay analysis of landslide susceptibility maps and historical landslides shows that the RF model based on 15 m resolution raster units can obtain the ...