LEADER 01883nam  2200349z- 450 
001 9910557104303321
005 20231214133702.0
035   $a(CKB)5400000000041004
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69084
035   $a(EXLCZ)995400000000041004
100   $a20202105d2020      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEnsemble Algorithms and Their Applications
210   $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020
215   $a1 electronic resource (182 p.)
311   $a3-03936-958-X 
311   $a3-03936-959-8 
330   $aIn recent decades, the development of ensemble learning methodologies has gained a significant attention from the scientific and industrial community, and found their application in various real-word problems. Theoretical and experimental evidence proved that ensemble models provide a considerably better prediction performance than single models. The main aim of this collection is to present the recent advances related to ensemble learning algorithms and investigate the impact of their application in a diversity of real-world problems. All papers possess significant elements of novelty and introduce interesting ensemble-based approaches, which provide readers with a glimpse of the state-of-the-art research in the domain.
606   $aInformation technology industries$2bicssc
615  7$aInformation technology industries
700   $aPintelas$b Panagiotis E$4edt$01296132
702   $aLivieris$b Ioannis E$4edt
702   $aPintelas$b Panagiotis E$4oth
702   $aLivieris$b Ioannis E$4oth
906   $aBOOK
912   $a9910557104303321
996   $aEnsemble Algorithms and Their Applications$93023791
997   $aUNINA