| 000 | 03835naaaa2201129uu 4500 | ||
|---|---|---|---|
| 003 | BUT | ||
| 005 | 20230412165422.0 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20230405s2023 xx |||||o ||| eng|| d | ||
| 020 | _a9783036567884 | ||
| 020 | _a9783036567891 | ||
| 040 |
_aoapen _coapen |
||
| 041 | 0 | _aeng | |
| 080 | _a004 | ||
| 100 | 1 |
_aKonys, Agnieszka _4edt |
|
| 245 | 1 | 0 | _aKnowledge Engineering and Data Mining |
| 260 |
_aBasel _bMDPI - Multidisciplinary Digital Publishing Institute _c2023 |
||
| 300 | _a1 electronic resource (308 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aKnowledge engineering and data mining are fundamental topics in the area of artificial intelligence and knowledge-based systems. This Special Issue covers the entire knowledge engineering pipeline: from data acquisition and data mining to knowledge extraction and exploitation. The reader will find topics including data mining methods, multidimensional data analysis, supervised and unsupervised learning methods, methods of knowledge-based management, language ontologies, ontology learning, and others. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc |
||
| 546 | _aEnglish | ||
| 650 | 0 |
_aАнализ данных _94529 |
|
| 650 | 0 |
_aИскусственный интеллект _94518 |
|
| 653 | _acomputing-intensive data | ||
| 653 | _adynamic programming | ||
| 653 | _aloop nest tiling | ||
| 653 | _aparallel code | ||
| 653 | _aOpenMP C/C++ | ||
| 653 | _acredit scoring | ||
| 653 | _acash loans | ||
| 653 | _amachine learning | ||
| 653 | _adecision model | ||
| 653 | _aclassification | ||
| 653 | _afeature selection | ||
| 653 | _aresampling | ||
| 653 | _adiscretization | ||
| 653 | _aknowledge representation | ||
| 653 | _aformal ontologies | ||
| 653 | _agraph databases | ||
| 653 | _aevaluation feature selection | ||
| 653 | _aevaluation model | ||
| 653 | _apsychosocial education | ||
| 653 | _arecommendation systems | ||
| 653 | _arank aggregation | ||
| 653 | _adifferential evolution | ||
| 653 | _asupervised learning | ||
| 653 | _amatrix factorization | ||
| 653 | _ametaheuristic | ||
| 653 | _aclinical named entity recognition | ||
| 653 | _aChinese medical text | ||
| 653 | _apre-trained model | ||
| 653 | _asystematic review | ||
| 653 | _amulticriteria | ||
| 653 | _aMCDA | ||
| 653 | _aMCDM | ||
| 653 | _aMADM | ||
| 653 | _aMODM | ||
| 653 | _aAHP | ||
| 653 | _aTOPSIS | ||
| 653 | _aVIKOR | ||
| 653 | _aPROMETHEE | ||
| 653 | _aANP | ||
| 653 | _acomputer-aided design (CAD) | ||
| 653 | _aeducational data mining | ||
| 653 | _aengineering education | ||
| 653 | _aonline and hybrid learning environments | ||
| 653 | _asocial media analytics | ||
| 653 | _asoft tissue | ||
| 653 | _agamma correction | ||
| 653 | _alandmark detection | ||
| 653 | _aX-ray images | ||
| 653 | _afacial profile | ||
| 653 | _aprediction | ||
| 653 | _aartificial neural network | ||
| 653 | _asupport vector machine | ||
| 653 | _arandom forest | ||
| 653 | _aregression | ||
| 653 | _aoffshore wave | ||
| 653 | _awind speed | ||
| 653 | _aunmanned aerial vehicle | ||
| 653 | _aUAV smoke show | ||
| 653 | _amobile networks | ||
| 653 | _aartificial intelligence | ||
| 653 | _ahealthcare | ||
| 653 | _adatabase design | ||
| 653 | _ageospatial data | ||
| 653 | _asoftware | ||
| 700 | 1 |
_aNowak-Brzezińska, Agnieszka _4edt |
|
| 700 | 1 |
_aKonys, Agnieszka _4oth |
|
| 700 | 1 |
_aNowak-Brzezińska, Agnieszka _4oth |
|
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/6944 _70 _zDownload |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/98891 _70 _zDescription |
| 909 |
_c4 _dDarya Shvetsova |
||
| 942 |
_2udc _cEE |
||
| 999 |
_c6432 _d6432 |
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