movement of sediment in the river causes many changes in the river bed. these changes are called bedform. river bedform has significant and direct effects on bed roughness, flow resistance, and water surface profile. thus, having adequate knowledge of the bedform is of special importance in river engineering. several methods have been developed by researchers for estimation of bed form dimensio...

This paper deals with the identification of nonlinear systems using multi-kernel approach. In this context, we have improved the Support Vector Regression (SVR) method in order to identify nonlinear complex system. Our idea consists in dividing the regressor vector in several blocks, and, for each one a kernel function is used. This blockwise SVR approach is called Support Kernel Regression (SK...

This paper developed a systematic strategy establishing RBF on the wavelet analysis, which includes continuous and discrete RBF orthonormal wavelet transforms respectively in terms of singular fundamental solutions and nonsingular general solutions of differential operators. In particular, the harmonic Bessel RBF transforms were presented for high-dimensional data processing. It was also found ...

These kernels combine the benefits of two other important classes of kernels: the homogeneous additive kernels (e.g. the χ2 kernel) and the RBF kernels (e.g. the exponential kernel). However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited to linear kernels. Recently, Maji and Berg [2] and Vedaldi and Zisserman [4] proposed exp...

The Gaussian radial basis function (RBF) is a widely used kernel in kernel-based methods. parameter RBF, referred to as the shape parameter, plays an essential role model fitting. In this paper, we propose method select parameters for general RBF kernel. It can simultaneously serve variable selection and regression estimation. For former, asymptotic consistency established; latter, estimation e...

Compared to linear kernel, nonlinear kernels can often substantially improve the accuracies of many machine learning algorithms. In this paper, we compare 5 different nonlinear kernels: minmax, RBF, fRBF (folded RBF), acos, and acos-χ, on a wide range of publicly available datasets. The proposed fRBF kernel performs very similarly to the RBF kernel. Both RBF and fRBF kernels require an importan...

In this paper, the effects of using multi RBF kernel for an online LSSVR on modeling and control performance are investigated. The Jacobian information of the system is estimated via online LSSVR model. Kernel parameter determines how the measured input is mapped to the feature space and a better plant model can be achieved by discarding redundant features. Therefore, introducing flexibility in...

The GMM (generalized min-max) kernel was recently proposed [5] as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the GMM kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GCWS) to convert the GMM kernel to linear kernel. We call this approach as “GMM-GCWS”. In the machine learning l...

Corresponding Author: Fikri Budiman Department of Computer Science, University of Dian Nuswantoro, Semarang, Indonesia Email: [email protected] Abstract: Image retrieval using Support Vector Machine (SVM) classification very depends on kernel function and parameter. Kernel function used by dot product substitution from old dimension feature to new dimension depends on image dataset ...