LEADER 04796nam 2201297z- 450 001 9910557324603321 005 20231214133621.0 035 $a(CKB)5400000000042633 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76309 035 $a(EXLCZ)995400000000042633 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Methods with Noisy, Incomplete or Small Datasets 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (316 p.) 311 $a3-0365-1288-8 311 $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 $aopen contours 610 $asimilarly shaped fish species 610 $aDiscrete Cosine Transform (DCT) 610 $aDiscrete Fourier Transform (DFT) 610 $aExtreme Learning Machines (ELM) 610 $afeature engineering 610 $asmall data-sets 610 $aoptimization 610 $amachine learning 610 $apreprocessing 610 $aimage generation 610 $aweighted interpolation map 610 $abinarization 610 $asingle sample per person 610 $aroot canal measurement 610 $amultifrequency impedance 610 $adata augmentation 610 $aneural network 610 $afunctional magnetic resonance imaging 610 $aindependent component analysis 610 $adeep learning 610 $arecurrent neural network 610 $afunctional connectivity 610 $aepisodic memory 610 $asmall sample learning 610 $afeature selection 610 $anoise elimination 610 $aspace consistency 610 $alabel correlations 610 $aempirical mode decomposition 610 $asparse representations 610 $atensor decomposition 610 $atensor completion 610 $amachine translation 610 $apairwise evaluation 610 $aeducational data 610 $asmall datasets 610 $anoisy datasets 610 $asmart building 610 $aInternet of Things (IoT) 610 $aMarkov Chain Monte Carlo (MCMC) 610 $aontology 610 $agraph model 610 $aArtificial Neural Network 610 $aDiscriminant Analysis 610 $adengue 610 $afeature extraction 610 $asound event detection 610 $anon-negative matrix factorization 610 $aultrasound images 610 $ashadow detection 610 $ashadow estimation 610 $aauto-encoders 610 $asemi-supervised learning 610 $aprediction 610 $afeature importance 610 $afeature elimination 610 $ahierarchical clustering 610 $aParkinson?s disease 610 $afew-shot learning 610 $apermutation-variable importance 610 $atopological data analysis 610 $apersistent entropy 610 $asupport-vector machine 610 $adata science 610 $aintelligent decision support 610 $asocial vulnerability 610 $agender-gap 610 $adigital-gap 610 $aCOVID19 610 $apolicy-making support 610 $aartificial intelligence 610 $aimperfect dataset 615 7$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