Comparision of Neural Algorithms for Funchtion Approximation

Efficient Approximation Algorithms for Point-set Diameter in Higher Dimensions

We study the problem of computing the diameter of a  set of $n$ points in $d$-dimensional Euclidean space for a fixed dimension $d$, and propose a new $(1+varepsilon)$-approximation algorithm with $O(n+ 1/varepsilon^{d-1})$ time and $O(n)$ space, where $0 < varepsilonleqslant 1$. We also show that the proposed algorithm can be modified to a $(1+O(varepsilon))$-approximation algorithm with $O(n+...

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)

Constructive Neural Network Algorithms for Function Approximation Tasks

The generalization capability and training time of conventional neural networks depend on their architecture. In conventional neural networks, we have to define the architecture prior to training but in constructive neural network (CoNN) algorithms the network architecture is constructed during the training process. This paper presents an overview of CoNN algorithms that constructing feedforwar...

Comparision of profile retrieval algorithms for MAX-DOAS

Introduction Conclusions References Tables Figures