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Function approximation

Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management

Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management

... Dynamic resource allocation problems span dynamic vehicle routing (Gendreau and Potvin, 1998; Ichoua et al., 2005), where there has been recent interest in the application of approximate dynamic programming for the ...

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Design of Hybrid Fuzzy Neural Network for Function Approximation

Design of Hybrid Fuzzy Neural Network for Function Approximation

... for function approximation is ...ferent approximation problems: sine-cosine function approximation and Narazaki-Ralescu function and shows its natural capability of inference, ...

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Why deep neural networks for function approximation

Why deep neural networks for function approximation

... • We have shown that, for ε-approximation of functions with enough piecewise smoothness, a multilayer neural network which uses Θ(log(1/ε)) layers only needs O (poly log(1/ε)) neurons, while Ω(poly(1/ε)) neurons ...

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Finite Mixture Estimation Algorithm for Arbitrary Function Approximation

Finite Mixture Estimation Algorithm for Arbitrary Function Approximation

... Basis Function (RBF) networks emerged as a variant of artificial neural networks in the late ...functional approximation, spline interpolation and mixture models ...excellent approximation ...

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Robust Approximate Bilinear Programming for Value Function Approximation

Robust Approximate Bilinear Programming for Value Function Approximation

... Value function approximation methods have been successfully used in many applications, but the prevailing techniques often lack useful a priori error ...value function approximation, which ...

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Corrected Fourier series and its application to function approximation

Corrected Fourier series and its application to function approximation

... correction function are collectively re- ferred to as the corrected Fourier ...to function approximation; the procedures to determine the coe ffi cients of the corrected Fourier series are illustrated ...

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The existence and local behaviour of the quadratic function approximation

The existence and local behaviour of the quadratic function approximation

... This paper analyses the local behaviour of the quadratic function approximation to a function which has a given power series expansion about the origin.. It is shown that the quadratic H[r] ...

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FAemb: a function approximation-based embedding method for image retrieval

FAemb: a function approximation-based embedding method for image retrieval

... for function approximation and it is more straightfor- ward to achieve a higher order approximation of f at anchor points by this ...the function approximation process, will be used as ...

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Neural Networks  A General Framework for Non Linear Function Approximation

Neural Networks A General Framework for Non Linear Function Approximation

... non-linear function approximation where the form of the mapping is governed by a number of adjustable ...regression function that we wish to ...

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Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting

Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting

... distance approximation should be ...of function ap- proximation, the issue of approximating functions with multiple branches for the same input value, which often appear in practical applications, has to be ...

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Evolutionary Function Approximation for Reinforcement Learning

Evolutionary Function Approximation for Reinforcement Learning

... value function can be represented as a ...a function approximator, which represents the mapping from state-action pairs to values via a more concise, parameterized function and uses supervised ...

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Function Approximation Using Robust Radial Basis Function Networks

Function Approximation Using Robust Radial Basis Function Networks

... Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust regression for dealing with ...

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The divergence of reinforcement learning algorithms with value-iteration and function approximation

The divergence of reinforcement learning algorithms with value-iteration and function approximation

... The divergence results of this paper were derived for quadratic critic functions, as this was the situation that allowed for easiest analysis to derive concrete divergence examples. We assume that similar divergence ...

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Recursive Identification of Hammerstein Systems with Polynomial Function Approximation

Recursive Identification of Hammerstein Systems with Polynomial Function Approximation

... Abstract: Nonlinear system identification is considered, where the nonlinear static function was approximated by a number of polynomial functions. It is based on a piecewise-linear Hammerstein model, which is ...

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Some qualitative results for the quadratic function approximation

Some qualitative results for the quadratic function approximation

... Particular attention was paid to the size of the region over which this was a good approximation and to comparisons of the accuracy of the quadratic approximation with the more tradition[r] ...

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More qualitative results for the quadratic function approximation

More qualitative results for the quadratic function approximation

... Hermite-Fade approximation which were addressed in [2] are examined in further ...the approximation. This leads us to examine the approximation over a much larger region than was the case in ...4) ...

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The role of the loss function in the probabilistic function approximation

The role of the loss function in the probabilistic function approximation

... The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item.. Where records identify the[r] ...

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ABOUT AN ALGORITHM OF FUNCTION APPROXIMATION BY THE LINEAR SPLINES

ABOUT AN ALGORITHM OF FUNCTION APPROXIMATION BY THE LINEAR SPLINES

... Problems of asymptotically optimal nodes where splines may be coupled with selection and determine the order of the best spline approximations with increasing number of nodes are solved for different kinds of splines ...

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Approximation of function and its derivatives using radial basis function networks

Approximation of function and its derivatives using radial basis function networks

... basis function based on a set of 250 test ...approximate function obtained from all networks are quite low and hence the global shape of the orig- inal function is well captured as shown in Figure ...

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Positive operators and approximation in function spaces on completely regular spaces

Positive operators and approximation in function spaces on completely regular spaces

... positive approximation processes can be defined in terms of mathematical expectation of suitable random variables taking their values into a Borel subset of some Euclidean ...the approximation processes can ...

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