The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

In this paper we use radial basis functions to solve multivariable integral equations. We use collocation method for implementation. Numerical experiments show the accuracy of the method.

in this research, the spatial distribution of electrical conductivity and ph of springs, ghanats and the base flow concentration places on the streams of central and south-west and central parts of the hamedan-bahar plain were evaluated. using different geostatistical methods such as kriging, minimum curvature, inverse distance, natural neighbor, local polynomial and radial basis functions, 108...

The reconstruction of spectral function from correlation in Euclidean space is a challenging task. In this paper, we employ the Machine Learning techniques terms radial basis functions networks to reconstruct finite number data. To test our method, first generate one type data using mock by mixing several Breit-Wigner propagators. We found that compared with other traditional methods, TSVD, Tik...

Gas turbine design, development, monitoring and maintenance are widely based on numerical simulations of the steady and transient engine performance. Most of the equations that are solved in the simulation programs involve the thermodynamic properties of the fluid flowing through the engine. These properties depend on temperature, pressure, humidity and fuel dosage. As the solution of chemical ...

in this paper we use radial basis functions to solve multivariable integral equations. we use collocation method for implementation. numerical experiments show the accuracy of the method.

‎Radial basis functions (RBFs) are a powerful tool for approximating the solution of high-dimensional problems‎. ‎They are often referred to as a meshfree method and can be spectrally accurate‎. ‎In this paper, we analyze a new stable method for evaluating Gaussian radial basis function interpolants based on the eigenfunction expansion‎. ‎We develop our approach in two-dimensional spaces for so...