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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| Formaat: | Elektronisch Hoofdstuk |
| Taal: | Engels |
| Gepubliceerd in: |
De Gruyter
2016
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| Online toegang: | DOAB: download the publication DOAB: description of the publication |
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| 008 | 20210211s2016 xx |||||o ||| 0|eng d | ||
| 020 | |a 9783110481068 | ||
| 020 | |a 9783110481068 | ||
| 040 | |a oapen |c oapen | ||
| 024 | 7 | |a 10.1515/9783110481068 |c doi | |
| 041 | 0 | |a eng | |
| 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.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 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 DOAB: download the publication |
| 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 |