00868nam0 2200265 450 00002160320090115132536.020090115d1977----km-y0itay50------baitaITy-------001yyOrganizzazione: teoria e metodoguida all'indagine sui problemi organizzativiBruno MaggiMilanoISEDI1977VIII, 151 p.23 cmEconomia e direzione aziendale442001Economia e direzione aziendaleOrganizzazione: teoria e metodo46552AziendeOrganizzazioneStrategia65818Maggi,Bruno070114165ITUNIPARTHENOPE20090115RICAUNIMARC000021603611/104877NAVA22009Organizzazione: teoria e metodo46552UNIPARTHENOPE01654nam2 22003133i 450 SUN009652620140127101202.6170.0020140122d1988 |0itac50 baitaIT|||| |||||ˆ5: ‰Modificazioni ed estinzione del rapportoaspettativa, comando e distacco, dimissioni, collocamento a riposo, dispensa, decadenza, indennità di buonuscitaBruno ArenaNapoli : Jovene1988XVI721 p. ; 25 cmFondo Tribunale di Napoli.001SUN00045472001 ˆIl ‰rapporto di pubblico impiego nella legislazione e nella giurisprudenzacommentario sistematicodiretto da Bruno Balletti5210 NapoliJovene215 v.25 cm.Impiego pubblicoDiritto del lavoroFISUNC000769NapoliSUNL000005Arena, BrunoSUNV076744409606JoveneSUNV000014650ITSOL20181109RICASUN0096526UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00 CONS FTA.1 (5) 00 FTN16 bis UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00 CONS FTA.1 (5) 00 FTN6774 UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZAFTN16CONS FTA.1 (5) bispaUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZAFTN6774CONS FTA.1 (5)paModificazioni ed estinzione del rapporto982184UNICAMPANIA04796nam 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