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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| 格式: | Online |
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| 语言: | 英语 |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| 在线阅读: | ONIX_20220111_9783036526928_946 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-77114 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-771142024-03-30T12:51:09Z Statistical Data Modeling and Machine Learning with Applications Gocheva-Ilieva, Snezhana 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries 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. 2022-01-11T13:52:39Z 2022-01-11T13:52:39Z 2021 book ONIX_20220111_9783036526928_946 9783036526928 9783036526935 https://directory.doabooks.org/handle/20.500.12854/77114 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4733 https://mdpi.com/books/pdfview/book/4733 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2693-5 10.3390/books978-3-0365-2693-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036526928 9783036526935 184 Basel, Switzerland open access |
| spellingShingle | 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries Statistical Data Modeling and Machine Learning with Applications |
| title | Statistical Data Modeling and Machine Learning with Applications |
| title_full | Statistical Data Modeling and Machine Learning with Applications |
| title_fullStr | Statistical Data Modeling and Machine Learning with Applications |
| title_full_unstemmed | Statistical Data Modeling and Machine Learning with Applications |
| title_short | Statistical Data Modeling and Machine Learning with Applications |
| title_sort | statistical data modeling and machine learning with applications |
| topic | 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | 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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20220111_9783036526928_946 |