04796nam 2201297z- 450 991055732460332120231214133621.0(CKB)5400000000042633(oapen)https://directory.doabooks.org/handle/20.500.12854/76309(EXLCZ)99540000000004263320202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMachine Learning Methods with Noisy, Incomplete or Small DatasetsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic 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 industriesbicsscopen contourssimilarly shaped fish speciesDiscrete Cosine Transform (DCT)Discrete Fourier Transform (DFT)Extreme Learning Machines (ELM)feature engineeringsmall data-setsoptimizationmachine learningpreprocessingimage generationweighted interpolation mapbinarizationsingle sample per personroot canal measurementmultifrequency impedancedata augmentationneural networkfunctional magnetic resonance imagingindependent component analysisdeep learningrecurrent neural networkfunctional connectivityepisodic memorysmall sample learningfeature selectionnoise eliminationspace consistencylabel correlationsempirical mode decompositionsparse representationstensor decompositiontensor completionmachine translationpairwise evaluationeducational datasmall datasetsnoisy datasetssmart buildingInternet of Things (IoT)Markov Chain Monte Carlo (MCMC)ontologygraph modelArtificial Neural NetworkDiscriminant Analysisdenguefeature extractionsound event detectionnon-negative matrix factorizationultrasound imagesshadow detectionshadow estimationauto-encoderssemi-supervised learningpredictionfeature importancefeature eliminationhierarchical clusteringParkinson’s diseasefew-shot learningpermutation-variable importancetopological data analysispersistent entropysupport-vector machinedata scienceintelligent decision supportsocial vulnerabilitygender-gapdigital-gapCOVID19policy-making supportartificial intelligenceimperfect datasetInformation 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