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IEEE/ICACT20210173 Slide.09        [Big Slide]	Oral Presentation
	I'll talk about data shaping. The collected data are shaped to ensure that the network model is easy to learn. When training a data set whose scales are not uniform among the features, there is a demerit that the training parameters update becomes slow, and the learning becomes slow. Since data shaping has the merit of reducing the number of steps to find the optimum solution and improving the accuracy. In this study, there are three methods: standardization, normalization, and binary translation.

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