Algorithms and Models for the Web Graph, Anthony Bonato; Fan Chung Graham; Pawe? Pra?at
Автор: David J. C. MacKay Название: Information Theory, Inference and Learning Algorithms ISBN: 0521642981 ISBN-13(EAN): 9780521642989 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This exciting and entertaining textbook is ideal for courses in information, communication and coding. It is an unparalleled entry point to these subjects for professionals working in areas as diverse as computational biology, data mining, financial engineering and machine learning.
Автор: Sung Название: Algorithms in Bioinformatics ISBN: 1420070339 ISBN-13(EAN): 9781420070330 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents an introduction to the algorithmic techniques applied in bioinformatics. For each topic, this title details the biological motivation, defines the corresponding computational problems, and includes examples to illustrate each algorithm.
Автор: Konstantin Avratchenkov; Debora Donato; Nelly Litv Название: Algorithms and Models for the Web-Graph ISBN: 3540959947 ISBN-13(EAN): 9783540959946 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The papers address a wide variety of topics related to the study of the Web-graph such as theoretical and empirical analysis of the Web graph and Web 2.0 graphs, random walks on the Web and Web 2.0 graphs and their applications, and design and performance evaluation of the algorithms for social networks.
Описание: Presents the suitable algorithms to convert collected data in order to gain knowledge of the condition of an infrastructure for maintenance scheduling purposes. This book addresses developments in time response series and its derivatives including the wavelet-based impulse response function.
Автор: Nancy A. Lynch Название: Distributed Algorithms, ISBN: 1558603484 ISBN-13(EAN): 9781558603486 Издательство: Elsevier Science Рейтинг: Цена: 20549.00 р. Наличие на складе: Поставка под заказ.
Описание: A guide to designing, implementing and analyzing distributed algorithms. It covers problems including resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election and global snapshots.
Автор: Bonato Название: Algorithms and Models for the Web Graph ISBN: 3319497863 ISBN-13(EAN): 9783319497860 Издательство: Springer Рейтинг: Цена: 5870.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The workshop gathered the researchers who are working on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet.
Автор: Mukhopadhyay Sambit, Morris Edward, Arulkumaran Sa Название: Algorithms for Obstetrics and Gynaecology ISBN: 0199651396 ISBN-13(EAN): 9780199651399 Издательство: Oxford Academ Рейтинг: Цена: 7760.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Algorithms in Obstetrics and Gynaecology presents the core knowledge needed to tackle all situations in obstetrics and gynaecology, in a structured fashion. All algorithms are designed to support rapid decision making in the most clinically relevant situations to minimise the risks of a poor outcome.
Автор: Anthony Bonato; Michael Mitzenmacher; Pawel Pralat Название: Algorithms and Models for the Web Graph ISBN: 3319035355 ISBN-13(EAN): 9783319035352 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: They address topics related to graph-theoretic and algorithmic aspects of related complex networks, including citation networks, social networks, biological networks, molecular networks and other networks arising from the Internet.
Автор: Anthony Bonato; Fan R.K. Chung Название: Algorithms and Models for the Web-Graph ISBN: 3540770038 ISBN-13(EAN): 9783540770039 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 5th International Workshop on Algorithms and Models for the Web-Graph, WAW 2007, held in San Diego, CA, USA, in December 2007 - colocated with WINE 2007, the Third International Workshop on Internet and Network Economics. This book presents 13 revised full papers and 5 revised short papers.
Автор: Nachtsheim;Neter;Kutner Название: Applied Linear Statistical Models with Student CD ISBN: 0071122214 ISBN-13(EAN): 9780071122214 Издательство: McGraw-Hill Рейтинг: Цена: 9265.00 р. Наличие на складе: Поставка под заказ.
Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
Автор: Fuente, Angel de la. Название: Mathematical methods and models for economists ISBN: 0521585295 ISBN-13(EAN): 9780521585293 Издательство: Cambridge Academ Рейтинг: Цена: 8554.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is intended as a textbook for a first-year PhD course in mathematics for economists and as a reference for graduate students in economics. It provides a self-contained, rigorous treatment of most of the concepts and techniques required to follow the standard first-year theory sequence in micro and macroeconomics.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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