Graphs for Pattern Recognition
This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as b...
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| 格式: | Online |
| 語言: | 英语 |
| 出版: |
De Gruyter
2021
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| 主題: | |
| 在線閱讀: | https://library.oapen.org/handle/20.500.12657/46036 |
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| _version_ | 1869527593042051072 |
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| author | Gainanov, Damir |
| author_browse | Gainanov, Damir |
| author_facet | Gainanov, Damir |
| author_sort | Gainanov, Damir |
| collection | Directory of Open Access Books |
| description | This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition.
Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology.
The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions. |
| format | Online |
| id | doab-20.500.12854ir-30837 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | De Gruyter |
| publisherStr | De Gruyter |
| record_format | ojs |
| spelling | doab-20.500.12854ir-308372025-07-30T18:22:26Z Graphs for Pattern Recognition Gainanov, Damir Computers Artificial Intelligence Computer Vision & Pattern Recognition Technology & Engineering Agriculture thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQV Computer vision thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions. 2021-02-10T13:45:23Z 2021-02-10T13:45:23Z 2021-01-12T04:31:59Z 2016 book https://library.oapen.org/handle/20.500.12657/46036 9783110481068 https://directory.doabooks.org/handle/20.500.12854/30837 eng open access image/jpeg image/jpeg image/jpeg image/jpeg n/a n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf De Gruyter De Gruyter https://doi.org/10.1515/9783110481068 https://doi.org/10.1515/9783110481068 af2fbfcc-ee87-43d8-a035-afb9d7eef6a5 Knowledge Unlatched 9783110481068 Knowledge Unlatched (KU) KU Select 2019: STEM Backlist Books De Gruyter open access |
| spellingShingle | Computers Artificial Intelligence Computer Vision & Pattern Recognition Technology & Engineering Agriculture thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQV Computer vision thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming Gainanov, Damir Graphs for Pattern Recognition |
| title | Graphs for Pattern Recognition |
| title_full | Graphs for Pattern Recognition |
| title_fullStr | Graphs for Pattern Recognition |
| title_full_unstemmed | Graphs for Pattern Recognition |
| title_short | Graphs for Pattern Recognition |
| title_sort | graphs for pattern recognition |
| topic | Computers Artificial Intelligence Computer Vision & Pattern Recognition Technology & Engineering Agriculture thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQV Computer vision thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming |
| topic_facet | Computers Artificial Intelligence Computer Vision & Pattern Recognition Technology & Engineering Agriculture thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQV Computer vision thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TV Agriculture and farming |
| url | https://library.oapen.org/handle/20.500.12657/46036 |
| work_keys_str_mv | AT gainanovdamir graphsforpatternrecognition |