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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Detalhes bibliográficos
Autor Corporativo: Gocheva-Ilieva, Snezhana
Formato: Livro
Idioma:Inglês
Publicado em: MDPI - Multidisciplinary Digital Publishing Institute 2021
Assuntos:
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX 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
n/a
Acesso em linha:https://directory.doabooks.org/handle/20.500.12854/77114
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