Описание: The book introduces readers to some of the latest advances in and approaches to decision-making methods based on thermodynamic characters and hesitant fuzzy linguistic preference relations.
Автор: Juan D. Vel?squez; Vasile Palade; Lakhmi C. Jain Название: Advanced Techniques in Web Intelligence-2 ISBN: 364243035X ISBN-13(EAN): 9783642430350 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents tasks for gathering information on and interpreting user behavior in Web based systems, ubiquitous environments, social networks and traditional Web applications, in order to create new systems that personalize the Web user experience.
Автор: Karl Schlechta Название: Formal Methods for Nonmonotonic and Related Logics ISBN: 3319896520 ISBN-13(EAN): 9783319896526 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The two volumes in this advanced textbook present results, proof methods, and translations of motivational and philosophical considerations to formal constructions. In this Vol. I the author explains preferential structures and abstract size. In the associated Vol. II he presents chapters on theory revision and sums, defeasible inheritance theory, interpolation, neighbourhood semantics and deontic logic, abstract independence, and various aspects of nonmonotonic and other logics.In both volumes the text contains many exercises and some solutions, and the author limits the discussion of motivation and general context throughout, offering this only when it aids understanding of the formal material, in particular to illustrate the path from intuition to formalisation. Together these books are a suitable compendium for graduate students and researchers in the area of computer science and mathematical logic.
Описание: This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC. It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints. With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.
Автор: Ewa Orlowska Название: Incomplete Information: Rough Set Analysis ISBN: 3790810495 ISBN-13(EAN): 9783790810493 Издательство: Springer Рейтинг: Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is an account of the current status of the basic theory, extensions and applications of rough sets. The book presents rough set formalisms and methods of modelling and handling incomplete information, and motivates their applicability to knowledge discovery and machine learning.
Автор: Wu Название: Robust Latent Feature Learning for Incomplete Big Data ISBN: 9811981396 ISBN-13(EAN): 9789811981395 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
Описание: The book introduces readers to some of the latest advances in and approaches to decision-making methods based on thermodynamic characters and hesitant fuzzy linguistic preference relations.
Автор: Stephane P. Demri; Ewa Orlowska Название: Incomplete Information: Structure, Inference, Complexity ISBN: 3642075401 ISBN-13(EAN): 9783642075407 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph presents a systematic, exhaustive and up-to-date overview of formal methods and theories for data analysis and inference inspired by the concept of rough set. The formalisms developed are non-invasive in that only the actual information that is needed in the process of analysis without external sources of information being required.
Автор: Johannes F?rnkranz; Eyke H?llermeier Название: Preference Learning ISBN: 3642422306 ISBN-13(EAN): 9783642422300 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The first book dedicated to this new branch of machine learning and data mining, this comprehensive treatment, which covers everything from label ranking to preference learning and recommender systems, will be required reading for researchers working in AI.
Автор: Ewa Orlowska Название: Incomplete Information: Rough Set Analysis ISBN: 3790824577 ISBN-13(EAN): 9783790824575 Издательство: Springer Рейтинг: Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In 1982, Professor Pawlak published his seminal paper on what he called "rough sets" - a work which opened a new direction in the development of theories of incomplete information.
Автор: Janos Fodor; Bernard De Baets; Patrice Perny Название: Preferences and Decisions under Incomplete Knowledge ISBN: 3790824747 ISBN-13(EAN): 9783790824742 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Nowadays, decision problems are pervaded with incomplete knowledge, i.e., imprecision and/or uncertain information, both in the problem description and in the preferential information.
Автор: Atanu Sengupta; Tapan Kumar Pal Название: Fuzzy Preference Ordering of Interval Numbers in Decision Problems ISBN: 3642100600 ISBN-13(EAN): 9783642100604 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text studies different real decision situations where problems are defined in inexact environment. It presents the latest research in fuzzy preference ordering of interval numbers and modeling of interval decision problems.
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