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Extensions to projection pursuit learning networks with parametric smoothers

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 2002
A neural network which can grow its own structure on the training attracts a lot of research. Projection pursuit learning networks (PPLNs) and cascaded correlation learning networks (CCLNs) are two such neural networks. Unlike a CCLN where cascaded connections from the existing hidden units to the new candidate hidden unit are required to establish ...
null Shyh-Rong Lay +2 more
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An Extension of Alias Sampling Method for Parametrized Probability Distributions

Journal of Computational Physics, 2000
In this very interesting paper an extension of the alias sampling technique for distribution functions depending on a number of parameters is developed. It takes advantage of modern computer architectures with large amount of cheap memory, by using discrete representations of probability distribution functions.
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Parametric Galois Extensions and Their Spectral Invariants

In this article we develop a dynamic formulation of Galois theory in which the classical symmetry group of a polynomial is replaced by a parametrized family of groups. The construction centers on a dependency space: a parameter set $\Gamma$ that determines, at each stage, which roots are admitted via a boundary condition in the base field.
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Parametrizing group extension loops

Communications in Algebra, 2000
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An extensible highly parametric open-source wingbox generation framework