000			02217naaaa2200541uu 4500
003			BUT
005			20230420092029.0
006			m o d
007			cr|mn|---annan
008			202301s2022 x |||||o ||||eng|| d
020			_a9783110785968
020			_a9783110785968
020			_a9783110785951
020			_a9783110786132
040			_aoapen _coapen
041	0		_aeng
080			_a004.65
100	1		_aMorik, Katharina _4edt
245	1	0	_aDiscovery in Physics
260			_aBerlin/Boston _bDe Gruyter _c2022
300			_a1 electronic resource (349 p.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aDe Gruyter STEM
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aVolume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc
546			_aEnglish
650		0	_aБазы данных _92229
653			_aResource-Constrained Data Analysis
653			_aResource-Aware Machine Learning
653			_aEmbedded Systems and Machine Learning
653			_aBig Data and Machine Learning
653			_aArtificial Intelligence
653			_aHighly Distributed Data
653			_aML on Small devices Data mining for Ubiquitous System
653			_aSoftware Cyber-physical systems
653			_aMachine learning in high-energy physics
653			_aMachine learning for knowledge discovery
700	1		_aRhode, Wolfgang _4edt
700	1		_aMorik, Katharina _4oth
700	1		_aRhode, Wolfgang _4oth
830			_94546 _aDe Gruyter STEM
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/96449 _70 _zDescription
909			_c255 _dRobiyakhon Olimjonova
942			_2udc _cEE
999			_c6447 _d6447