In mathematics a radial basis function (RBF) is a real-valued function φ {\textstyle \varphi } whose value depends only on the distance between the input...

In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In...

mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the...

In mathematics, a basis function is an element of a particular basis for a function space. Every function in the function space can be represented as...

Radial basis function (RBF) interpolation is an advanced method in approximation theory for constructing high-order accurate interpolants of unstructured...

In mathematics, a radial function is a real-valued function defined on a Euclidean space ⁠ R n {\displaystyle \mathbb {R} ^{n}} ⁠ whose value at each...

radial basis functions. Given a set of control points { c i , i = 1 , 2 , … , K } {\displaystyle \{c_{i},i=1,2,\ldots ,K\}} , a radial basis function...

feedforward networks. Radial basis functions are functions that have a distance criterion with respect to a center. Radial basis functions have been applied...

space of functions in which point evaluation is a continuous linear functional. Specifically, a Hilbert space H {\displaystyle H} of functions from a set...

special class of activation functions known as radial basis functions (RBFs) are used in RBF networks. These activation functions can take many forms, but...

S2CID 53715158. Approximation theory Fitness approximation Kriging Least squares (function approximation) Radial basis function network v t e v t e...

random feature matrices of size O ( D N ) {\textstyle O(DN)} . The radial basis function (RBF) kernel on two samples x i , x j ∈ R d {\displaystyle x_{i}...

patterns in the feature space. Bell-shaped function Cauchy distribution Normal distribution Radial basis function kernel Squires, G. L. (2001-08-30). Practical...

{\displaystyle \nu } is an integer limiting values should be taken, (see ). Radial basis function Genton, Marc G. (1 March 2002). "Classes of kernels for machine...

activation functions have been proposed, including the rectifier and softplus functions. More specialized activation functions include radial basis functions (used...

distance weighting Natural-neighbor interpolation Spline interpolation Radial basis function interpolation Barnes interpolation Bilinear interpolation Bicubic...

distance through a Gaussian RBF, obtains the hidden layer of a radial basis function network. This use of k-means has been successfully combined with...

basis Hilbert's basis theorem Generating set of a group Base (topology) Change of basis Greedoid Normal basis Polynomial basis Radial basis function Standard...

{\displaystyle k} K ( x , x k ) {\displaystyle K(x,x_{k})} is the Radial basis function kernel (Gaussian kernel) as formulated below. K ( x , x k ) = e...

(statistics) Lagrange interpolation Missing data Newton–Cotes formulas Radial basis function interpolation Simple rational approximation Smoothing Sheppard,...

Pointed space, a topological space with a distinguished point Radial basis function, a function depending only on the distance from the origin Madsen, David...

_{j})=(\mathbf {x} _{i}\cdot \mathbf {x} _{j}+r)^{d}} . Gaussian radial basis function: k ( x i , x j ) = exp ⁡ ( − γ ‖ x i − x j ‖ 2 ) {\displaystyle...

Park, J.; Sandberg, I. W. (1991). "Universal Approximation Using Radial-Basis-Function Networks". Neural Computation. 3 (2): 246–257. doi:10.1162/neco...

A polyharmonic spline is a linear combination of polyharmonic radial basis functions (RBFs) denoted by φ {\displaystyle \varphi } plus a polynomial term:...

modeling Hierarchical modulation Hierarchical proportion Hierarchical radial basis function Hierarchical storage management Hierarchical task network Hierarchical...

clusters as features, perhaps after transforming them through a radial basis function (a technique that has been used to train RBF networks). Coates and...

likely to be treated as K separate scalar-output basis functions). An example of this is radial basis functions (RBF's), which compute some transformed version...

visual recognition problems, which became known as "deep learning". Radial basis function and wavelet networks were introduced in 2013. These can be shown...

first attempt to extend radial basis function (RBF), which was then quite popular in scattered data processing and function approximation, to the solution...

In machine learning, a Hyper basis function network, or HyperBF network, is a generalization of radial basis function (RBF) networks concept, where the...