Results 231 to 240 of about 99,709 (264)

Animated deformations with radial basis functions

Proceedings of the ACM symposium on Virtual reality software and technology - VRST '00, 2000
We present a novel approach to creating deformations of polygonal models using Radial Basis Functions (RBFs) to produce localized real-time deformations. Radial Basis Functions assume surface smoothness as a minimal constraint and animations produce smooth displacements of affected vertices in a model.
Jun-yong Noh, Douglas Fidaleo, Ulrich Neumann   +2 more
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Computationally Efficient Radial Basis Function

2018
We introduced a Square-law based RBF kernel called SQuare RBF (SQ-RBF) which is computationally efficient and effective due to the elimination of the exponential term. In contrast to the Gaussian RBF, SQ-RBF requires smaller computational operation count and direct implementation without a call to higher order library.
Adedamola Wuraola, Nitish D. Patel
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Radial Basis Functions

1995
Abstract The network models discussed in Chapters 3 and 4 are based on units which compute a non-linear function of the scalar product of the input vector and a weight vector. Here we consider the other major class of neural network model, in which the activation of a hidden unit is determined by the distance between the input vector and
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Radial Basis Functions

2017
In this chapter the RBF mathematical concepts are exposed considering firstly the interpolation problem with the RBF function defined by known values at source points; a first hands-on example is provided showing how RBF work. Further topics of RBF theory are then introduced considering the differentiation of RBF, the fitting of an RBF with known ...
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Radial Basis Functions

2004
Radial basis functions are traditionaland powerful tools for multivariate scattered data interpolation.Much of the material presented in this chapter is essentially needed in the subsequent developments of this work,such as for the multi level approximation schemes in Chapter 5, and the mesh free simulation of transport processes in Chapter 6.
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Indexed families of functionals, and Gaussian radial basis functions

The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002., 2003
We report on results concerning the capabilities of gaussian radial basis function networks in the setting of inner product spaces that need not be finite dimensional. Specifically, we show that important indexed families of functionals can be uniformly approximated, with the approximation uniform also with respect to the index.
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Experiments on Ensembles of Radial Basis Functions

2004
Building an ensemble of classifiers is an useful way to improve the performance. In the case of neural networks the bibliography has centered on the use of Multilayer Feedforward (MF). However, there are other interesting networks like Radial Basis Functions (RBF) that can be used as elements of the ensemble.
Carlos Hernández-Espinosa, Mercedes Fernández-Redondo, Joaquín Torres-Sospedra   +2 more
openaire   +1 more source

Radial Basis Functions: A Bayesian Treatment.

1998
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Barber, D., Schottky, B.
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On Monotonic Radial Basis Function Networks

IEEE Transactions on Cybernetics
This article deals with monotonicity conditions for radial basis function (RBF) networks. Two architectures of RBF networks are considered-1) unnormalized network with a local character of the basis function and 2) a normalized network where the value of RBF is taken relatively with respect to the others.
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Orthogonal least squares learning algorithm for radial basis function networks

IEEE Transactions on Neural Networks, 1991
C F N Cowan, P M Grant
exaly