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The phrase "function approximation" is both correct and commonly used in written English.
Function approximation refers to the process of estimating or calculating an unknown function using techniques such as regression or interpolation. It is often used in mathematics, computer science, and engineering to model complex systems or phenomena. Example: "The researchers used a neural network to perform function approximation and predict the stock market trends with high accuracy."
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Approximate dynamic programming (ADP) commonly employs value function approximation to numerically solve complex dynamic programming problems.
In a case a function approximation the output of the neural network is a functional value.
Friedman, J. H. Greedy function approximation: a gradient boosting machine.
Friedman J. H. Greedy function approximation: a gradient boosting machine.
Function approximation is also analyzed.
Vapnik, V., Golowich, S. E. & Smola, A. Support vector method for function approximation, regression estimation, and signal processing.
The main aim is obtaining high function approximation accuracy.
Four models were generated by Genetic Function Approximation (GFA).
Function approximation and prediction are obtained from neural networks.
ApproximationDemo.m is a demonstration program that does function approximation with interpolation, Hermite interpolation, and clamped splines.
B. Panda, M. Riedewald, S. B. Pope, J. Gehrke, L. P. Chew, Indexing for Function Approximation, in Proceedings of Int.
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CEO of Professional Science Editing for Scientists @ prosciediting.com