Statistical Data Modeling and Machine Learning with Applications

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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Bibliographic Details
Other Authors: Gocheva-Ilieva, Snezhana (Editor)
Format: Book Chapter
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
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
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Summary: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 added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.
Physical Description:1 electronic resource (184 p.)
ISBN:books978-3-0365-2693-5
9783036526928
9783036526935
Access:Open Access

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