Graphs for Pattern Recognition. Infeasible Systems of Linear Inequalities
Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision r...
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| Format: | Book Chapter |
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De Gruyter
2016
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 01225naaaa2200229uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_48863 | ||
| 005 | 20210211 | ||
| 020 | |a 9783110481068 | ||
| 020 | |a 9783110481068 | ||
| 024 | 7 | |a 10.1515/9783110481068 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Gainanov, Damir |4 auth | |
| 245 | 1 | 0 | |a Graphs for Pattern Recognition. Infeasible Systems of Linear Inequalities |
| 260 | |b De Gruyter |c 2016 | ||
| 300 | |a 1 electronic resource (148 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision rules for pattern recognition. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://doi.org/10.1515/9783110481068 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/48863 |7 0 |z DOAB: description of the publication |