In this tutorial paper, we give an overview about extensions of pattern recognition towards non-standard data which are not contained in a finite dimensional ...
Aug 22, 2024 · In this tutorial paper, we give an overview about extensions of pattern recognition towards non-standard data which are not contained in a ...
This tutorial paper gives an overview about extensions of pattern recognition towards non-standard data which are not contained in a finite dimensional ...
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Hammer B, Jain BJ. Neural methods for non-standard data. In: Verleysen M, ed. European Symposium on Artificial Neural Networks'2004. D-side publications; 2004: ...
Artificial neural networks, like many statistical methods, are capable of processing vast amounts of data and making predictions that are sometimes surprisingly ...
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In this section we demonstrate the selection of data in time series so that they can be used for learning the underlying dynamics. We use the method of sliding ...
Meanwhile, deep neural networks offer multiple advantages over traditional machine learning methods. First, these methods are highly flexible [25], allow ...
May 20, 2024 · Neural networks, powerful pattern recognition tools, excel in handling large-scale data and possess strong nonlinear fitting capabilities, ...
Jan 14, 2020 · Deep learning techniques are not always used because simpler models can perform better under certain circumstances.
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May 19, 2024 · We applied our techniques to estimate the likelihood of a nonlinear HANK model using US data. Standard macroeconomic data can identify the ...