Inference and Learning from Data: Volume 3: Learning, Ali H. Sayed
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.
Автор: Judea Pearl Название: Probabilistic Reasoning in Intelligent Systems, ISBN: 1558604790 ISBN-13(EAN): 9781558604797 Издательство: Elsevier Science Рейтинг: Цена: 9599.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.
The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Автор: Wojciech Wieczorek Название: Grammatical Inference ISBN: 3319468006 ISBN-13(EAN): 9783319468006 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed.
Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others.
Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>
Описание: The authors of this monograph survey a suite of techniques based on the theory of polynomials, collectively referred to as polynomial methods. These techniques provide useful tools for the design of highly practical algorithms with provable optimality, and for establishing the fundamental limits of inference problems through moment matching.
Автор: Wieczorek Wojciech Название: Grammatical Inference: Algorithms, Routines and Applications ISBN: 3319835890 ISBN-13(EAN): 9783319835891 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners.
Описание: This thesis focuses on the problem of optimizing the quality of network multimedia services.
Автор: Denis Bosq; Hung T. Nguyen Название: A Course in Stochastic Processes ISBN: 9048147131 ISBN-13(EAN): 9789048147137 Издательство: Springer Рейтинг: Цена: 35079.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Having in mind a mixed audience of students from different departments (Math- ematics, Statistics, Economics, Engineering, etc.) we have presented the material in each lesson in the most simple way, with emphasis on moti- vation of concepts, aspects of applications and computational procedures.
Автор: Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshn Название: Secure Networked Inference with Unreliable Data Sources ISBN: 9811323119 ISBN-13(EAN): 9789811323119 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
Автор: Ali H. Sayed Название: Inference and Learning from Data: Volume 2: Inference ISBN: 1009218263 ISBN-13(EAN): 9781009218269 Издательство: Cambridge Academ Рейтинг: Цена: 12355.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Автор: Ali H. Sayed Название: Inference and Learning from Data: Volume 1: Foundations ISBN: 1009218123 ISBN-13(EAN): 9781009218122 Издательство: Cambridge Academ Рейтинг: Цена: 13939.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in inference and learning. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning, and engineering.
Автор: Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshn Название: Secure Networked Inference with Unreliable Data Sources ISBN: 9811347654 ISBN-13(EAN): 9789811347658 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
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