Описание: This book investigates how transactions can be integrated with concurrent object-oriented programming, and how transactions can be made available to an application programmer at the programming language level. The book gives a detailed overview of existing transaction models, and analyzes their suitability for concurrent programming languages. A new transaction model named "Open Multithreaded Transactions" is presented. It provides features for controlling and structuring not only access to objects, as usual in transaction systems, but also threads taking part in transactions. Integration with exception handling makes open multithreaded transactions ideal building blocks for fault-tolerant applications. The book also describes the design of an object-oriented framework providing the necessary run-time support for open multithreaded transactions. Procedural, object-oriented and aspect-oriented interfaces for the application programmer are presented. Programming examples include code in Ada, Java and AspectJ.
Автор: Schapire Robert E., Freund Yoav Название: Boosting: Foundations and Algorithms ISBN: 0262526034 ISBN-13(EAN): 9780262526036 Издательство: MIT Press Рейтинг: Цена: 6772.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones.
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.
This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well.
The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
Автор: G?rtner, Matousek Название: Approximation Algorithms and Semidefinite Programming ISBN: 3642220142 ISBN-13(EAN): 9783642220142 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Semidefinite programs constitute one of the largest classes of optimization problems that can be solved with reasonable efficiency - both in theory and practice. They play a key role in a variety of research areas, such as combinatorial optimization, approximation algorithms, computational complexity, graph theory, geometry, real algebraic geometry and quantum computing. This book is an introduction to selected aspects of semidefinite programming and its use in approximation algorithms. It covers the basics but also a significant amount of recent and more advanced material. There are many computational problems, such as MAXCUT, for which one cannot reasonably expect to obtain an exact solution efficiently, and in such case, one has to settle for approximate solutions. For MAXCUT and its relatives, exciting recent results suggest that semidefinite programming is probably the ultimate tool. Indeed, assuming the Unique Games Conjecture, a plausible but as yet unproven hypothesis, it was shown that for these problems, known algorithms based on semidefinite programming deliver the best possible approximation ratios among all polynomial-time algorithms. This book follows the “semidefinite side” of these developments, presenting some of the main ideas behind approximation algorithms based on semidefinite programming. It develops the basic theory of semidefinite programming, presents one of the known efficient algorithms in detail, and describes the principles of some others. It also includes applications, focusing on approximation algorithms.
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