Data Structures and Algorithms, C. V. Sastry, Rakesh Nayak, CH Rajaramesh

Автор: Barab?si
Название: Network Science
ISBN: 1107076269 ISBN-13(EAN): 9781107076266
Издательство: Cambridge Academ
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Цена: 51750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of disciplines from physics to the social sciences, is the only book needed for an introduction to network science. In modular format, with clear delineation between undergraduate and graduate material, its unique design is supported by extensive online resources.

Автор: Strang
Название: Algorithms for Global Positioning
ISBN: 0980232732 ISBN-13(EAN): 9780980232738
Издательство: Cambridge Academ
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Цена: 76020.00 T
Наличие на складе: Невозможна поставка.
Описание: The emergence of satellite technology has changed the lives of millions of people. In particular, GPS has brought an unprecedented level of accuracy to the field of geodesy. This text is a guide to the algorithms and mathematical principles that account for the success of GPS technology and replaces the authors' previous work, Linear Algebra, Geodesy, and GPS (1997). An initial discussion of the basic concepts, characteristics and technical aspects of different satellite systems is followed by the necessary mathematical content which is presented in a detailed and self-contained fashion. At the heart of the matter are the positioning algorithms on which GPS technology relies, the discussion of which will affirm the mathematical contents of the previous chapters. Numerous ready-to-use MATLAB codes are included for the reader. This comprehensive guide will be invaluable for engineers and academic researchers who wish to master the theory and practical application of GPS technology.

Автор: Li, Fanzhang / Zhang, Li / Zhang, Zhao
Название: Dynamic Fuzzy Machine Learning
ISBN: 3110518708 ISBN-13(EAN): 9783110518702
Издательство: Walter de Gruyter
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Цена: 149590.00 T
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Описание: Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.

Автор: Trefethen
Название: Approximation Theory and Approximation Practice
ISBN: 1611972396 ISBN-13(EAN): 9781611972399
Издательство: Mare Nostrum (Eurospan)
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Цена: 55440.00 T
Наличие на складе: Невозможна поставка.
Описание: In a book that will appeal to beginners and experts alike, Oxford University’s Nick Trefethen presents approximation theory using a fresh approach for this established field.Approximation Theory and Approximation Practice is a textbook on classical polynomial and rational approximation theory for the twenty-first century. It uses MATLAB? to teach the field’s most important ideas and results and differs fundamentally from other works on approximation theory in a number of ways: its emphasis is on topics close to numerical algorithms; concepts are illustrated with Chebfun; and each chapter is a PUBLISHable Matlab M-file, available online.In addition, the book centers on theorems and methods for analytic functions, which appear so often in applications, rather than on functions at the edge of discontinuity with their seductive theoretical challenges. Original sources are cited rather than textbooks, and each item in the bibliography is accompanied by an editorial comment.Each chapter has a collection of exercises, which span a wide range from mathematical theory to Chebfun-based numerical experimentation.

Автор: Voulgaris Zacharias
Название: Julia for Data Science
ISBN: 1634621301 ISBN-13(EAN): 9781634621304
Издательство: Gazelle Book Services
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Цена: 41330.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

Название: Handbook of Approximation Algorithms and Metaheuristics
ISBN: 1498770150 ISBN-13(EAN): 9781498770156
Издательство: Taylor&Francis
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Цена: 433840.00 T
Наличие на складе: Нет в наличии.
Описание: This handbook reflects the tremendous growth in the field, over the past two decades. Through contributions from leading experts, this handbook provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics.

Автор: Landsberg JM
Название: Geometry and Complexity Theory
ISBN: 1107199239 ISBN-13(EAN): 9781107199231
Издательство: Cambridge Academ
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Цена: 64410.00 T
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Описание: A comprehensive introduction to algebraic geometry and representation theory written by a leading expert in the field. For graduate students and researchers in computer science and mathematics, the book demonstrates state-of-the-art techniques to solve real world problems, focusing on P vs NP and the complexity of matrix multiplication.

Автор: Bilan, Stepan,
Название: Formation methods, models, and hardware implementation of pseudorandom number generators :
ISBN: 1522527737 ISBN-13(EAN): 9781522527732
Издательство: Mare Nostrum (Eurospan)
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Цена: 189030.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Modern computing systems preserve all information in intricate binary codes. The evolution of systems and technologies that aid in this preservation process must be continually assessed to ensure that they are keeping up with the demands of society. Formation Methods, Models, and Hardware Implementation of Pseudorandom Number Generators: Emerging Research and Opportunities is a crucial scholarly resource that examines the current methodologies used in number generator construction, and how they pertain to the overall advancement of contemporary computer systems. Featuring coverage on relevant topics such as cellular automata theory, inhomogeneous cells, and sequence generators, this publication is ideal for software engineers, computer programmers, academicians, students, and researchers that are interested in staying abreast of innovative trends within the computer engineering field.