It is well-known that model combination can improve prediction performance of regression model. We investigate the model combination of Support Vector Regression (SVR) with regularization path in this paper. We first define Lε-risk of SVR, and prove that SVR regularization path leads to at least one Lε-risk consistent fitted model. Then we establish the Lε-risk consistency for convex combinatio...

Lakes are major surface water resource in semi-arid regions, providing for agriculture and domestic use. Prediction of future availability lakes regions is important as they highly sensitive to climate variability. This study examine the level fluctuations Pakhal Lake, Telangana, India using a combination process-based hydrological model machine learning technique under change scenarios. an art...

Wavelet theory has a profound impact on signal processing as it offers a rigorous mathematical framework to the treatment of multiresolution problems. The combination of soft computing and wavelet theory has led to a number of new techniques. On the other hand, as a new generation of learning algorithms, support vector regression (SVR) was developed by Vapnik et al. recently, in which ε-insensi...

Support vector regression (SVR) has been regarded as a state-of-the-art method for approximation and regression. The importance of kernel function, which is so-called admissible support vector kernel (SV kernel) in SVR, has motivated many studies on its composition. The Gaussian kernel (RBF) is regarded as a “best” choice of SV kernel used by non-expert in SVR, whereas there is no evidence, exc...

Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussia...

The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach co...

Support vector regression (SVR) is a learning method based on the support vector machine (SVM) that can be used for curve fitting and function estimation. In this paper, the ability of the nu-SVR to predict the catalytic activity of the Fischer-Tropsch (FT) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (MLP) and subtractiv...