Robust Latent Feature Learning for Incomplete Big Data, Wu
Автор: Richard G. Delisle Название: Charles Darwin`s Incomplete Revolution ISBN: 3030172023 ISBN-13(EAN): 9783030172022 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a thorough reanalysis of Charles Darwin's Origin of Species, which for many people represents the work that alone gave rise to evolutionism. Of course, scholars today know better than that. Yet, few resist the temptation of turning to the Origin in order to support it or reject it in light of their own work. Apparently, Darwin fills the mythical role of a founding figure that must either be invoked or repudiated. The book is an invitation to move beyond what is currently expected of Darwin's magnum opus. Once the rhetorical varnish of Darwin's discourses is removed, one discovers a work of remarkably indecisive conclusions. The book comprises two main theses:(1) The Origin of Species never remotely achieved the theoretical unity to which it is often credited. Rather, Darwin was overwhelmed by a host of phenomena that could not fit into his narrow conceptual framework.(2) In the Origin of Species, Darwin failed at completing the full conversion to evolutionism. Carrying many ill-designed intellectual tools of the 17th and 18th centuries, Darwin merely promoted a special brand of evolutionism, one that prevented him from taking the decisive steps toward an open and modern evolutionism.It makes an interesting read for biologists, historians and philosophers alike.
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: 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: Branka Stojanovi?; Oge Marques; Aleksandar Ne?kovi Название: Segmentation and Separation of Overlapped Latent Fingerprints ISBN: 3030233634 ISBN-13(EAN): 9783030233631 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.
By offering a structured overview of the most important approaches currently available, putting them in perspective, and suggesting numerous resources for further exploration, this book gives its readers a clear path for learning new topics and engaging in related research. Written from a technical perspective, and yet using language and terminology accessible to non-experts, it describes the technologies, introduces relevant datasets, highlights the most important research results in each area, and outlines the most challenging open research questions.
This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information.
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: 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: Emmanuel Vincent; Arie Yeredor; Zbyn?k Koldovsk?; Название: Latent Variable Analysis and Signal Separation ISBN: 3319224816 ISBN-13(EAN): 9783319224817 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. Five special topics are addressed: tensor-based methods for blind signal separation;
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.
Описание: A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.
Описание: Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.
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