Machine Learning Methods with Noisy, Incomplete or Small Datasets

• Autore: Solé-Casals Jordi
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (316 p.)
• Soggetto topico: Information technology industries
• Soggetto non controllato: artificial intelligence; Artificial Neural Network; auto-encoders; binarization; COVID19; data augmentation; data science; deep learning; dengue; digital-gap; Discrete Cosine Transform (DCT); Discrete Fourier Transform (DFT); Discriminant Analysis; educational data; empirical mode decomposition; episodic memory; Extreme Learning Machines (ELM); feature elimination; feature engineering; feature extraction; feature importance; feature selection; few-shot learning; functional connectivity; functional magnetic resonance imaging; gender-gap; graph model; hierarchical clustering; image generation; imperfect dataset; independent component analysis; intelligent decision support; Internet of Things (IoT); label correlations; machine learning; machine translation; Markov Chain Monte Carlo (MCMC); multifrequency impedance; neural network; noise elimination; noisy datasets; non-negative matrix factorization; ontology; open contours; optimization; pairwise evaluation; Parkinson's disease; permutation-variable importance; persistent entropy; policy-making support; prediction; preprocessing; recurrent neural network; root canal measurement; semi-supervised learning; shadow detection; shadow estimation; similarly shaped fish species; single sample per person; small data-sets; small datasets; small sample learning; smart building; social vulnerability; sound event detection; space consistency; sparse representations; support-vector machine; tensor completion; tensor decomposition; topological data analysis; ultrasound images; weighted interpolation map
• Persona (resp. second.): SunZhe; CaiafaCesar F; Marti-PuigPere; TanakaToshihisa; Solé-CasalsJordi
• Sommario/riassunto: 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.
• Titolo autorizzato: Machine Learning Methods with Noisy, Incomplete or Small Datasets
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557324603321
• Lo trovi qui: Univ. Federico II