TY  - GEN
AU  - Gocheva-Ilieva,Snezhana
AU  - Gocheva-Ilieva,Snezhana
TI  - Statistical Data Modeling and Machine Learning with Applications
SN  - books978-3-0365-2693-5
PY  - 2021///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Information technology industries
KW  - bicssc
KW  - mathematical competency
KW  - assessment
KW  - machine learning
KW  - classification and regression tree
KW  - CART ensembles and bagging
KW  - ensemble model
KW  - multivariate adaptive regression splines
KW  - cross-validation
KW  - dam inflow prediction
KW  - long short-term memory
KW  - wavelet transform
KW  - input predictor selection
KW  - hyper-parameter optimization
KW  - brain-computer interface
KW  - EEG motor imagery
KW  - CNN-LSTM architectures
KW  - real-time motion imagery recognition
KW  - artificial neural networks
KW  - banking
KW  - hedonic prices
KW  - housing
KW  - quantile regression
KW  - data quality
KW  - citizen science
KW  - consensus models
KW  - clustering
KW  - Gower's interpolation formula
KW  - Gower's metric
KW  - mixed data
KW  - multidimensional scaling
KW  - classification
KW  - data-adaptive kernel functions
KW  - image data
KW  - multi-category classifier
KW  - predictive models
KW  - support vector machine
KW  - stochastic gradient descent
KW  - damped Newton
KW  - convexity
KW  - METABRIC dataset
KW  - breast cancer subtyping
KW  - deep forest
KW  - multi-omics data
KW  - categorical data
KW  - similarity
KW  - feature selection
KW  - kernel density estimation
KW  - non-linear optimization
KW  - kernel clustering
KW  - n/a
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/4733
UR  - https://directory.doabooks.org/handle/20.500.12854/77114
ER  -