The linearly separable problem is a fundamental problem in pattern classification. Firstly, from the perspective of spatial distribution, this paper focuses on the linear separability of a region dataset at the distribution level instead of the linearly separable issue between two datasets at the traditional category level. Firstly, the former can reflect the spatial distribution of real data, which is more helpful to its application in pattern classification. Secondly, based on spatial geometric theory, an adaptive construction method for testing the linear separability of a region dataset is demonstrated and designed. Finally, the corresponding computer algorithm is designed, and some simulation verification experiments are carried out based on some manual datasets and benchmark datasets. Experimental results show the correctness and effectiveness of the proposed method.
Pattern classification Linear separability Region datasets Geometric calculation
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This work was supported by the National Natural Science Foundation of China (71373131, 61402236, 61572259 and U1736105), Training Program of the Major Research Plan of the National Science Foundation of China (91546117).
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