Abstract In this study, wavelet support vector machine (WSWM) model is proposed for daily suspended sediment (SS) prediction. The WSVM model is achieved by combination of two methods; discrete wavelet analysis and support vector machine (SVM). The developed model was compared with single SVM. Daily discharge (Q) and SS data from Yadkin River at Yadkin College, NC station in the USA were used. I...

Stochastic coordinate descent, due to its practicality and efficiency, is increasingly popular in machine learning and signal processing communities as it has proven successful in several large-scale optimization problems , such as l1 regularized regression, Support Vector Machine, to name a few. In this paper, we consider a composite problem where the nonsmoothness has a general structure that...

Machine learning algorithms have been shown to be highly effective in solving optimization problems a wide range of applications. Such typically use gradient descent with backpropagation and the chain rule. Hence, fails if intermediate gradients are zero for some functions computational graph, because it causes collapse when multiplying zero. Vector quantization is one those challenging machine...

In scientific computing and machine learning applications, matrices more general multidimensional arrays (tensors) can often be approximated with the help of low-rank decompositions. Since tensors fixed rank form smooth Riemannian manifolds, one popular tools for finding approximations is to use optimization. Nevertheless, efficient implementation gradients Hessians, required in optimization al...

in this work some quantitative structure activity relationship models were developed for prediction of three bioenvironmental parameters of 28 volatile organic compounds, which are used in assessing the behavior of pollutants in soil. these parameters are; half-life, non dimensional effective degradation rate constant and effective péclet number in two type of soil. the most effective descripto...

The Support Vector Machine (SVM) is a new technique for solving various function estimation problems. We refer to function estimation as learning, and a technique for estimating the unknown function from data as a learning machine. To construct a learning machine one requires four components: a domain (a learning problem with associated loss function), an induction principle, a set of decision ...

In binary classification problems, two classes of data seem to be different from each other. It is expected more complicated due the clusters in class also tend different. Traditional algorithms as Support Vector Machine (SVM) or Twin (TWSVM) cannot sufficiently exploit structural information with cluster granularity data, cause limitation on capability simulation trends. Structural (S-TWSVM) e...