Parallel Genetic Algorithms, Gabriel Luque; Enrique Alba
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: Gibbons Название: Efficient Parallel Algorithms ISBN: 0521388414 ISBN-13(EAN): 9780521388412 Издательство: Cambridge Academ Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An introduction to the field of efficient parallel algorithms and to the techniques for efficient parallelization. It is self-contained and presumes no special knowledge of parallel computers or particular mathematics.
Автор: Aubanel Название: Elements of Parallel Computing ISBN: 1498727891 ISBN-13(EAN): 9781498727891 Издательство: Taylor&Francis Рейтинг: Цена: 10258.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Designed for introductory parallel computing courses at the advanced undergraduate or beginning graduate level, Elements of Parallel Computing presents the fundamental concepts of parallel computing not from the point of view of hardware, but from a more abstract view of algorithmic and implementation patterns. The aim is to facilitate the teaching of parallel programming by surveying some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. The presentation is friendly and informal. The content of the book is language neutral, using pseudocode that represents common programming language models.
The first five chapters present core concepts in parallel computing. SIMD, shared memory, and distributed memory machine models are covered, along with a brief discussion of what their execution models look like. The book also discusses decomposition as a fundamental activity in parallel algorithmic design, starting with a naive example, and continuing with a discussion of some key algorithmic structures. Important programming models are presented in depth, as well as important concepts of performance analysis, including work-depth analysis of task graphs, communication analysis of distributed memory algorithms, key performance metrics, and a discussion of barriers to obtaining good performance.
The second part of the book presents three case studies that reinforce the concepts of the earlier chapters. One feature of these chapters is to contrast different solutions to the same problem, using select problems that aren't discussed frequently in parallel computing textbooks. They include the Single Source Shortest Path Problem, the Eikonal equation, and a classical computational geometry problem: computation of the two-dimensional convex hull. After presenting the problem and sequential algorithms, each chapter first discusses the sources of parallelism then surveys parallel algorithms.
Автор: Francisco Fern?ndez de Vega; Jos? Ignacio Hidalgo Название: Parallel Architectures and Bioinspired Algorithms ISBN: 3642427367 ISBN-13(EAN): 9783642427367 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book outlines best practices when combining bioinspired algorithms with parallel architectures, describes recent work by leading researchers in the field and offers a map outlining the main paths already explored and new pathways toward the future.
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.
Описание: While the weight of a structure constitutes a significant part of the cost, a minimum weight design is not necessarily the minimum cost design. Little attention in structural optimization has been paid to the cost optimization problem, particularly of realistic three-dimensional structures.
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.
Автор: Arrems Hua; Shih-Liang Chang Название: Algorithms and Architectures for Parallel Processing ISBN: 3642030947 ISBN-13(EAN): 9783642030949 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 9th International Conference ICA3PP 2009 Taipei Taiwan June 811 2009 Proceedings. .
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