LEADER 01001nam--2200361---450- 001 990000701240203316 005 20051104093327.0 035 $a0070124 035 $aUSA010070124 035 $a(ALEPH)000070124USA01 035 $a0070124 100 $a20011022d1950----km-y0itay0103----ba 101 $ager 102 $aSE 105 $a||||||||001yy 200 1 $a<> Kabiren$fvon Bengt Hemberg 210 $aUppsala$cAlmquist & Wiksells Boktryckeri$d1950 215 $a420 p.$c2 c. di tav.$d24 cm 410 $12001 606 0 $aMitologia classica 676 $a292 700 1$aHEMBERG,$bBengt$0549089 801 0$aIT$bsalbc$gISBD 912 $a990000701240203316 951 $aII.2. 3846(VIII C 1261)$b97493 LM$cVIII C 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20011022$lUSA01$h1805 979 $c20020403$lUSA01$h1719 979 $aPATRY$b90$c20040406$lUSA01$h1648 979 $aCOPAT3$b90$c20051104$lUSA01$h0933 996 $aKabiren$9961704 997 $aUNISA LEADER 01208nam0 22002891i 450 001 UON00080163 005 20231205102425.971 010 $a83-901809-3-6 100 $a20020107d1997 |0itac50 ba 101 $aeng 102 $aPL 105 $a|||| ||||| 200 1 $aEthiopia and Alexandria$eThe metropolitan Episcopacy of Ethiopia$fStuart C. Munro-Hay 210 $aWarszawa$aWiesbaden$cScholz Nubica$d1997 215 $aXI, 240 p.$d23 cm 410 1$1001UON00067360$12001 $aBibliotheca nubica et aethiopica$eSchriftenreihe zur Kulturgeschichte des Raumes um das Rote Meer$fHerausgegeben von Piotr O. Scholz$v5 606 $aChiesa etiopica$3UONC018307$2FI 620 $aPL$dWarszawa$3UONL000573 676 $a281.7$cCHIESA COPTA E ABISSINA$v21 700 1$aMUNRO-HAY$bStuart C.$3UONV042657$0250849 712 $aScholz Nubica$3UONV258863$4650 801 $aIT$bSOL$c20250808$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00080163 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI VI A a 015 $eSI AA 20513 7 015 996 $aEthiopia and Alexandria$91301436 997 $aUNIOR LEADER 04811nam 2201309z- 450 001 9910557324603321 005 20220111 035 $a(CKB)5400000000042633 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76309 035 $a(oapen)doab76309 035 $a(EXLCZ)995400000000042633 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning Methods with Noisy, Incomplete or Small Datasets 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (316 p.) 311 08$a3-0365-1288-8 311 08$a3-0365-1287-X 330 $aIn many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios. 606 $aInformation technology industries$2bicssc 610 $aartificial intelligence 610 $aArtificial Neural Network 610 $aauto-encoders 610 $abinarization 610 $aCOVID19 610 $adata augmentation 610 $adata science 610 $adeep learning 610 $adengue 610 $adigital-gap 610 $aDiscrete Cosine Transform (DCT) 610 $aDiscrete Fourier Transform (DFT) 610 $aDiscriminant Analysis 610 $aeducational data 610 $aempirical mode decomposition 610 $aepisodic memory 610 $aExtreme Learning Machines (ELM) 610 $afeature elimination 610 $afeature engineering 610 $afeature extraction 610 $afeature importance 610 $afeature selection 610 $afew-shot learning 610 $afunctional connectivity 610 $afunctional magnetic resonance imaging 610 $agender-gap 610 $agraph model 610 $ahierarchical clustering 610 $aimage generation 610 $aimperfect dataset 610 $aindependent component analysis 610 $aintelligent decision support 610 $aInternet of Things (IoT) 610 $alabel correlations 610 $amachine learning 610 $amachine translation 610 $aMarkov Chain Monte Carlo (MCMC) 610 $amultifrequency impedance 610 $aneural network 610 $anoise elimination 610 $anoisy datasets 610 $anon-negative matrix factorization 610 $aontology 610 $aopen contours 610 $aoptimization 610 $apairwise evaluation 610 $aParkinson's disease 610 $apermutation-variable importance 610 $apersistent entropy 610 $apolicy-making support 610 $aprediction 610 $apreprocessing 610 $arecurrent neural network 610 $aroot canal measurement 610 $asemi-supervised learning 610 $ashadow detection 610 $ashadow estimation 610 $asimilarly shaped fish species 610 $asingle sample per person 610 $asmall data-sets 610 $asmall datasets 610 $asmall sample learning 610 $asmart building 610 $asocial vulnerability 610 $asound event detection 610 $aspace consistency 610 $asparse representations 610 $asupport-vector machine 610 $atensor completion 610 $atensor decomposition 610 $atopological data analysis 610 $aultrasound images 610 $aweighted interpolation map 615 7$aInformation technology industries 700 $aSolé-Casals$b Jordi$4edt$01303365 702 $aSun$b Zhe$4edt 702 $aCaiafa$b Cesar F$4edt 702 $aMarti-Puig$b Pere$4edt 702 $aTanaka$b Toshihisa$4edt 702 $aSolé-Casals$b Jordi$4oth 702 $aSun$b Zhe$4oth 702 $aCaiafa$b Cesar F$4oth 702 $aMarti-Puig$b Pere$4oth 702 $aTanaka$b Toshihisa$4oth 906 $aBOOK 912 $a9910557324603321 996 $aMachine Learning Methods with Noisy, Incomplete or Small Datasets$93026950 997 $aUNINA