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- W143570683 abstract "The problem of optimizing the size of a neural network for obtaining high classification accuracy in datasets is a hard problem. Existing studies provide theoretical upper bounds on the size of neural networks that are unrealistic to implement. Alternatively, optimizing empirically the neural network size may need a large number of experiments, which due to a considerable number of free parameters may become a real hard task in time and effort to accomplish. Multimedia datasets are usually large in size datasets because they are rich in training samples and rich in features that describe each sample. Working with neural networks and multimedia datasets will make even harder the task to optimize the neural network size. This work presents a mathematical formula for a priori calculating the size of a neural network for achieving high classification accuracy rate. This formula estimates neural networks size based only on the number of available training samples, resulting in sizes of neural networks that are realistic to implement. Using this formula in multimedia datasets aims to fix the size of an accurate neural network and allows researchers to concentrate on other aspects of their experiments." @default.
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- W143570683 date "2009-05-20" @default.
- W143570683 modified "2023-09-24" @default.
- W143570683 title "Neural networks and multimedia datasets: estimating the size of neural networks for achieving high classification accuracy" @default.
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