Fuzzy Statistical Decision-Making: Theory and Applications, Kahraman Cengiz, Kabak Цzgьr
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Huchang Liao; Zeshui Xu Название: Hesitant Fuzzy Decision Making Methodologies and Applications ISBN: 9811032645 ISBN-13(EAN): 9789811032646 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Preface.- 1 Hesitant Fuzzy Set and Its Extensions.- 2 Novel Correlation and Entropy Measures for Hesitant Fuzzy Set.- 3 Multiple Criteria Decision Making with Hesitant Fuzzy Hybrid Weighted Aggregation Operators.- 4 Hesitant Fuzzy Multiple Criteria Decision Making with Complete Weight Information.- 5 Hesitant Fuzzy Multiple Criteria Decision Making with Incomplete Weights.- 6 Decision Making with Hesitant Fuzzy Preference Relation.
Автор: S. Panchapakesan; N. Balakrishnan Название: Advances in Statistical Decision Theory and Applications ISBN: 1461274958 ISBN-13(EAN): 9781461274957 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The first editor`s association with Shanti Gupta goes back to 1965 when he came to Purdue to do his Ph.D. The second editor`s association with Shanti Gupta began in 1978 when he started his research in the area of order statistics. During the past twenty years, he has collaborated with Shanti Gupta on several publications.
Описание: This book integrates the type-2 fuzzy sets and multiple criteria decision making analysis in recent years and offers an authoritative treatise on the essential topics, both at the theoretical and applied end. In this book, some basic theory, type-2 fuzzy sets, methodology, algorithms, are introduced and then some compelling case studies in decision problems are covered in depth. The authors offer an authoritative treatise on the essential topics, both at the theoretical and applied end; In a systematic and logically organized way, the book exposes the reader to the essentials of the theory of type-2 fuzzy sets, methodology, algorithms, and their applications. Numerous techniques of decision making are carefully generalized by bringing the ideas of type-2 fuzzy sets; this concerns well-known methods including TOPSIS, Analytical Network Process, TODIM, and VIKOR. This book exposes the readers to the essentials of the theory of type-2 fuzzy sets, methodology, algorithms, and theirapplications.
Автор: Karthikrajan Senthilnathan, Balamurugan Shanmugam, Dinesh Goyal, Iyswarya Annapoorani, Ravi Samikannu Название: Deep Learning Applications and Intelligent Decision Making in Engineering ISBN: 1799821099 ISBN-13(EAN): 9781799821090 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 24948.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is designed for engineers, computer scientists, programmers, software engineers, researchers, academics, and students.
Автор: Karthikrajan Senthilnathan, Balamurugan Shanmugam, Dinesh Goyal, Iyswarya Annapoorani, Ravi Samikannu Название: Deep Learning Applications and Intelligent Decision Making in Engineering ISBN: 1799821080 ISBN-13(EAN): 9781799821083 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 32987.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process.
Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
Автор: Liao Huchang, Xu Zeshui Название: Hesitant Fuzzy Decision Making Methodologies and Applications ISBN: 9811098298 ISBN-13(EAN): 9789811098291 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Preface.- 1 Hesitant Fuzzy Set and Its Extensions.- 2 Novel Correlation and Entropy Measures for Hesitant Fuzzy Set.- 3 Multiple Criteria Decision Making with Hesitant Fuzzy Hybrid Weighted Aggregation Operators.- 4 Hesitant Fuzzy Multiple Criteria Decision Making with Complete Weight Information.- 5 Hesitant Fuzzy Multiple Criteria Decision Making with Incomplete Weights.- 6 Decision Making with Hesitant Fuzzy Preference Relation.
Автор: Kahraman Cengiz, Kutlu Gьndoğdu Fatma Название: Decision Making with Spherical Fuzzy Sets: Theory and Applications ISBN: 3030454606 ISBN-13(EAN): 9783030454609 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces readers to the novel concept of spherical fuzzy sets, showing how these sets can be applied in practice to solve various decision-making problems.
Описание: This book integrates the type-2 fuzzy sets and multiple criteria decision making analysis in recent years and offers an authoritative treatise on the essential topics, both at the theoretical and applied end.
Автор: Kahraman Название: Fuzzy Statistical Decision-Making ISBN: 3319390120 ISBN-13(EAN): 9783319390123 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.
Описание: Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques.
Описание: An examination of system reliability theory, this title features in-depth discussion of dependability management and reliability centered systems as well as functional safety issues-matters critical to the IEC standards.
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