@inproceedings{9308b2e69f704e3bb7868e1503893eea,
title = "An adaptive construction test method based on geometric calculation for linearly separable problems",
abstract = "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.",
keywords = "Geometric calculation, Linear separability, Pattern classification, Region datasets",
author = "Shuiming Zhong and Xiaoxiang Lu and Meng Li and Chengguang Liu and Yong Cheng and Sheng, {Victor S.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 4th International Conference on Cloud Computing and Security, ICCCS 2018 ; Conference date: 08-06-2018 Through 10-06-2018",
year = "2018",
doi = "10.1007/978-3-030-00021-9_36",
language = "English",
isbn = "9783030000202",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "392--405",
editor = "Elisa Bertino and Xingming Sun and Zhaoqing Pan",
booktitle = "Cloud Computing and Security - 4th International Conference, ICCCS 2018, Revised Selected Papers",
}