Results 231 to 240 of about 388,592 (291)

A neural network for traffic flow prediction with parallel processing of expanded convolutional and radial networks. [PDF]

Sci Rep
Ye W, Zheng Y, Bai H, Dai X.
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Breast Cancer Data Analysis Using Supervised Machine Learning Algorithms. [PDF]

Cureus
Kutal DH, Koseoglu BN.
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Radial basis function networks -- revisited [PDF]

, 2015
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A multiscale framework for developing equations of state of supercritical aqueous systems from synchrotron experiments, atomistic simulations, and machine learning

Karalis K, Churakov SV, Prasianakis N, Maxim F, Cervellino A, Temleitner L, Pusztai L, Tzampoglou P, Krack M, Ludwig C, Niceno B.   +10 more
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Robustness of radial basis functions

Neurocomputing, 2005
Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment.
Eickhoff, Ralf, Rückert, Ulrich
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Adaptive radial basis functions

Proceedings of 13th International Conference on Pattern Recognition, 1996
We develop adaptive radial basis functions: kernel-based models for regression and discrimination where the functional form of the basis function depends on the data. The approach may be regarded as a radial form of projection pursuit, with the additional constraint that the basis functions have a common functional form.
Andrew R. Webb, Simon Shannon
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Multi-layer radial basis function networks. An extension to the radial basis function

Proceedings of International Conference on Neural Networks (ICNN'96), 2002
This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron.
R. J. Craddock, K. Warwick
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On radial basis functions

2019
Many sciences and other areas of research and applications from engineering to economics require the approximation of functions that depend on many variables. This can be for a variety of reasons. Sometimes we have a discrete set of data points and we want to find an approximating function that completes this data; another possibility is that precise ...
Buhmann, Martin, Jäger, Janin
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