04811nam 2201309z- 450 991055732460332120220111(CKB)5400000000042633(oapen)https://directory.doabooks.org/handle/20.500.12854/76309(oapen)doab76309(EXLCZ)99540000000004263320202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning Methods with Noisy, Incomplete or Small DatasetsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (316 p.)3-0365-1288-8 3-0365-1287-X In 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.Information technology industriesbicsscartificial intelligenceArtificial Neural Networkauto-encodersbinarizationCOVID19data augmentationdata sciencedeep learningdenguedigital-gapDiscrete Cosine Transform (DCT)Discrete Fourier Transform (DFT)Discriminant Analysiseducational dataempirical mode decompositionepisodic memoryExtreme Learning Machines (ELM)feature eliminationfeature engineeringfeature extractionfeature importancefeature selectionfew-shot learningfunctional connectivityfunctional magnetic resonance imaginggender-gapgraph modelhierarchical clusteringimage generationimperfect datasetindependent component analysisintelligent decision supportInternet of Things (IoT)label correlationsmachine learningmachine translationMarkov Chain Monte Carlo (MCMC)multifrequency impedanceneural networknoise eliminationnoisy datasetsnon-negative matrix factorizationontologyopen contoursoptimizationpairwise evaluationParkinson's diseasepermutation-variable importancepersistent entropypolicy-making supportpredictionpreprocessingrecurrent neural networkroot canal measurementsemi-supervised learningshadow detectionshadow estimationsimilarly shaped fish speciessingle sample per personsmall data-setssmall datasetssmall sample learningsmart buildingsocial vulnerabilitysound event detectionspace consistencysparse representationssupport-vector machinetensor completiontensor decompositiontopological data analysisultrasound imagesweighted interpolation mapInformation technology industriesSolé-Casals Jordiedt1303365Sun ZheedtCaiafa Cesar FedtMarti-Puig PereedtTanaka ToshihisaedtSolé-Casals JordiothSun ZheothCaiafa Cesar FothMarti-Puig PereothTanaka ToshihisaothBOOK9910557324603321Machine Learning Methods with Noisy, Incomplete or Small Datasets3026950UNINA