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• W2892404744 abstract "In this paper, we address handwritten digit classification as a special problem of data compression modeling. The creation of the models—usually known as training—is just a process of counting. Moreover, the model associated to each class can be trained independently of all the other class models. Also, they can be updated later with new examples, even if the old ones are not available anymore. Under this framework, we show that it is possible to attain a classification accuracy consistently above 99.3% on the MNIST dataset, using classifiers trained in less than one hour on a common laptop." @default.
• W2892404744 created "2018-10-05" @default.
• W2892404744 creator A5051693451 @default.
• W2892404744 creator A5075489861 @default.
• W2892404744 date "2018-01-01" @default.
• W2892404744 modified "2023-09-26" @default.
• W2892404744 title "An Application of Data Compression Models to Handwritten Digit Classification" @default.
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• W2892404744 doi "https://doi.org/10.1007/978-3-030-01449-0_41" @default.
• W2892404744 hasPublicationYear "2018" @default.
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