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Machine Learning Methods with Noisy, Incomplete or Small Datasets

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, i...

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Bibliografiset tiedot
Yhteisötekijät:	Solé-Casals, Jordi, Sun, Zhe, Caiafa, Cesar F., Marti-Puig, Pere, Tanaka, Toshihisa
Aineistotyyppi:	Livro
Kieli:	Inglês
Julkaistu:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Aiheet:	thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries open contours similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset
Linkit:	https://directory.doabooks.org/handle/20.500.12854/76309
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!

Machine Learning Methods with Noisy, Incomplete or Small Datasets

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