Statistical Data Modeling and Machine Learning with Applications II.

2023

Author : Snezhana Gocheva-Ilieva
Release : 2023
Genre :
Kind : eBook
Book Rating : 016/5 ( reviews)

Book Synopsis Statistical Data Modeling and Machine Learning with Applications II. by : Snezhana Gocheva-Ilieva

Download or read book Statistical Data Modeling and Machine Learning with Applications II. written by Snezhana Gocheva-Ilieva. This book was released on 2023. Available in PDF, EPUB and Kindle. Book excerpt: The present reprint contains all of the articles in the second edition of the Special Issue titled “Statistical Data Modeling and Machine Learning with Applications II”. This Special Issue belongs to the “Mathematics and Computer Science” Section and aims to publish research on the theory and application of statistical data modeling and machine learning. New mathematical methods and approaches, new algorithms and research frameworks, and their applications aimed at solving diverse and nontrivial practical problems are proposed and developed in this SI. We believe that the chosen papers are attractive and useful to the international scientific community and will contribute to further research in the field of statistical data modeling and machine learning.

Statistical Data Modeling and Machine Learning with Applications

2021-12-21 Mathematics

Author : Snezhana Gocheva-Ilieva
Release : 2021-12-21
Genre : Mathematics
Kind : eBook
Book Rating : 928/5 ( reviews)

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Book Synopsis Statistical Data Modeling and Machine Learning with Applications by : Snezhana Gocheva-Ilieva

Download or read book Statistical Data Modeling and Machine Learning with Applications written by Snezhana Gocheva-Ilieva. This book was released on 2021-12-21. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Modeling in Machine Learning

2022-10-29 Computers

Author : Tilottama Goswami
Release : 2022-10-29
Genre : Computers
Kind : eBook
Book Rating : 527/5 ( reviews)

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Book Synopsis Statistical Modeling in Machine Learning by : Tilottama Goswami

Download or read book Statistical Modeling in Machine Learning written by Tilottama Goswami. This book was released on 2022-10-29. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more. Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials Presents a step-by-step approach from fundamentals to advanced techniques Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples

Statistical and Machine-Learning Data Mining

2012-02-28 Business & Economics

Author : Bruce Ratner
Release : 2012-02-28
Genre : Business & Economics
Kind : eBook
Book Rating : 216/5 ( reviews)

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Book Synopsis Statistical and Machine-Learning Data Mining by : Bruce Ratner

Download or read book Statistical and Machine-Learning Data Mining written by Bruce Ratner. This book was released on 2012-02-28. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Handbook of Computational Social Science, Volume 2

2021-11-10 Computers

Author : Uwe Engel
Release : 2021-11-10
Genre : Computers
Kind : eBook
Book Rating : 622/5 ( reviews)

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Book Synopsis Handbook of Computational Social Science, Volume 2 by : Uwe Engel

Download or read book Handbook of Computational Social Science, Volume 2 written by Uwe Engel. This book was released on 2021-11-10. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

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