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We then briefly introduce our ECG database in Section 11.1 and describe the convergence improvement and the accuracy of the classification in Section 11.2.
As for the structure of this paper, we start with a brief introduction of the one-dimensional UCM model [16], followed by a classification in Section 2.
With regard to our investigations and the time-dependent classification in Section 2, uniaxial drawing processes (DP) admit different sets of reasonable boundary conditions.
Based on this idea, we present two aspect models that extend PLSA model [22] for image patch classification in Section 6.
A valid ROI was obtained for color classification in Section 2. A window of 20 by 20 slides the ROI from left to right and top to bottom.
Based on these theoretical analyses, the L-band EMISAR polarimetric SAR data has been used to demonstrate application of MCSM and SVM to classification in Section 5. Finally, the conclusion and discussions are presented in Section 6.
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First, we investigate the performance of the approach using color classification only in Section 5.1.
b Hard decision tree with an additional module for halftone classification developed in Section 3.5.
The latter is done, by comparing their descriptions (or explanations) with our concurrency bug classification (given in Section 3.1).
Then we show some experimental results to verify the validity of our classification method in Section 4. In Section 5 we summarize the strength of our method.
In the first stage, hypotheses for vehicle positions are made using the results of appearance-based classification explained in Section 5.1.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com