Applied Algorithms, Prosenjit Gupta; Christos Zaroliagis

Автор: Cormen, Thomas H., E
Название: Introduction to algorithms 3 ed.
ISBN: 0262033844 ISBN-13(EAN): 9780262033848
Издательство: MIT Press
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Цена: 27588.00 р.
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Описание: A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-base flow.

Автор: Maria Bras-Amor?s; Tom H?holdt
Название: Applied Algebra, Algebraic Algorithms and Error-Correcting Codes
ISBN: 3642021808 ISBN-13(EAN): 9783642021800
Издательство: Springer
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Цена: 9781.00 р.
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Описание: This book constitutes the refereed proceedings of the 18th International Symposium on Applied Algebra, Algebraic Algorithms and Error-Correcting Codes, AAECC-18, held in Tarragona, Spain, in June 2009. Among the subjects addressed are block codes, including list-decoding algorithms; algebra and codes: rings, fields, algebraic geometry codes;

Автор: Xin-She Yang
Название: Nature-Inspired Algorithms and Applied Optimization
ISBN: 3319676687 ISBN-13(EAN): 9783319676685
Издательство: Springer
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Цена: 20962.00 р.
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Описание: Mathematical Analysis of Nature-Inspired Algorithms.- A Review of No Free Lunch Theorems, and their Implications for Metaheuristic Optimisation.- Global Convergence Analysis of Cuckoo Search Using Markov Theory.- On Effeciently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm.- Variants of the Flower Pollination Algorithm: A Review.

Автор: Niedermeier, Rolf
Название: Invitation to fixed-parameter algorithms
ISBN: 0198566077 ISBN-13(EAN): 9780198566076
Издательство: Oxford Academ
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Цена: 21780.00 р.
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Описание: An application-oriented introduction to the highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. Aimed at graduate and research mathematicians, algorithm designers, and computer scientists, it provides a fresh view on this highly innovative field of algorithmic research.

Автор: Daya Gaur; N.S. Narayanaswamy
Название: Algorithms and Discrete Applied Mathematics
ISBN: 3319530062 ISBN-13(EAN): 9783319530062
Издательство: Springer
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Цена: 9083.00 р.
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Описание: This book constitutes the proceedings of the Third International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2017, held in Goa, India, in February 2017. They deal with the following areas: algorithms, graph theory, codes, polyhedral combinatorics, computational geometry, and discrete geometry.

Автор: Sumit Ganguly; Ramesh Krishnamurti
Название: Algorithms and Discrete Applied Mathematics
ISBN: 3319149733 ISBN-13(EAN): 9783319149738
Издательство: Springer
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Цена: 7826.00 р.
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Описание: This book collects the refereed proceedings of the First International Conference onon Algorithms and Discrete Applied Mathematics, CALDAM 2015, held in Kanpur, India, in February 2015.

Автор: Sathish Govindarajan; Anil Maheshwari
Название: Algorithms and Discrete Applied Mathematics
ISBN: 331929220X ISBN-13(EAN): 9783319292205
Издательство: Springer
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Цена: 7826.00 р.
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Описание: This book collects the refereed proceedingsof the Second International Conference on Algorithms and Discrete AppliedMathematics, CALDAM 2016, held in Thiruvananthapuram, India, in February 2016.

Автор: Viktor M. Kureichik; Sergey P. Malioukov; Vladimir
Название: Genetic Algorithms for Applied CAD Problems
ISBN: 3540852808 ISBN-13(EAN): 9783540852803
Издательство: Springer
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Цена: 20962.00 р.
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Описание: With new perspective technologies of genetic search and evolution simulation at the heart of this book, the authors wanted to show how these technologies are used for the solution of practical problems. This monograph is devoted to specialists of CAD.

Автор: Schapire Robert E., Freund Yoav
Название: Boosting: Foundations and Algorithms
ISBN: 0262526034 ISBN-13(EAN): 9780262526036
Издательство: MIT Press
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Цена: 6772.00 р.
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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.