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By applying two preprocessing methods, data transformation and clustering, the data were prepared for the learning algorithms.
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We apply four preprocessing techniques.
Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise.
Furthermore, an improved algorithm with O(n(m+k)|S|) time is proposed by applying an efficient preprocessing for the MLCS problem.
The data reduction takes place at map level by applying preprocessing techniques for decoding the arrays, noise removal, and management of the overlapping peaks in the spectrum.
We applied two methods of data preprocessing, RMA and MAS5 algorithm, and compared the results (only RMA data are shown).
Motivated by our findings from the data analysis, we explore two preprocessing based algorithms ("Preprocessing based algorithms" section).
In this section, motivated by observations made in "Data observation" section, we introduce two preprocessing based algorithms that cover different design choices.
In the second approach, the transmission map enhancement can be achieved by applying a preprocessing filter to the hazy image.
For geometrical distortion and uneven intensity distribution of the acquired images by using a quad channel imager (see Fig. S1D), we applied two corrections to the images as preprocessing of linear unmixing.
We explore two preprocessing algorithms with theoretical justifications.
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