Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been a...

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Detalles Bibliográficos
Otros Autores:	Gocheva-Ilieva, Snezhana (Editor)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Materias:	Information technology industries mathematical competency assessment machine learning classification and regression tree CART ensembles and bagging ensemble model multivariate adaptive regression splines cross-validation dam inflow prediction long short-term memory wavelet transform input predictor selection hyper-parameter optimization brain-computer interface EEG motor imagery CNN-LSTM architectures real-time motion imagery recognition artificial neural networks banking hedonic prices housing quantile regression data quality citizen science consensus models clustering Gower's interpolation formula Gower's metric mixed data multidimensional scaling classification data-adaptive kernel functions image data multi-category classifier predictive models support vector machine stochastic gradient descent damped Newton convexity METABRIC dataset breast cancer subtyping deep forest multi-omics data categorical data similarity feature selection kernel density estimation non-linear optimization kernel clustering
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009654413206719