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Stochastic Algorithms for Visual Tracking, John MacCormick


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Цена: 107130.00T
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Автор: John MacCormick
Название:  Stochastic Algorithms for Visual Tracking
ISBN: 9781447111764
Издательство: Springer
Классификация:

ISBN-10: 1447111761
Обложка/Формат: Paperback
Страницы: 174
Вес: 0.27 кг.
Дата издания: 16.09.2011
Серия: Distinguished Dissertations
Язык: English
Размер: 234 x 156 x 10
Основная тема: Computer Science
Подзаголовок: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the curse of dimensionality suffered by standard particle filters.

Introduction to algorithms  3 ed.

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: 183920.00 T
Наличие на складе: Нет в наличии.
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.

Network Science

Автор: Barab?si
Название: Network Science
ISBN: 1107076269 ISBN-13(EAN): 9781107076266
Издательство: Cambridge Academ
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Цена: 51750.00 T
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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.

Algorithms for Data Science

Автор: Steele
Название: Algorithms for Data Science
ISBN: 3319457950 ISBN-13(EAN): 9783319457956
Издательство: Springer
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Цена: 83850.00 T
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Описание:

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:
(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.
(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.
(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Handbook of Approximation Algorithms and Metaheuristics

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.

Stochastic Algorithms: Foundations and Applications

Автор: Osamu Watanabe; Thomas Zeugmann
Название: Stochastic Algorithms: Foundations and Applications
ISBN: 3642049435 ISBN-13(EAN): 9783642049439
Издательство: Springer
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Цена: 65210.00 T
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Описание: This book constitutes the refereed proceedings of the 5th International Symposium on Stochastic Algorithms, Foundations and Applications, SAGA 2009, held in Sapporo, Japan, in October 2009. The 15 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 22 submissions.

Data Association for Multi-Object Visual Tracking

Автор: Margrit Betke, Zheng Wu
Название: Data Association for Multi-Object Visual Tracking
ISBN: 1627059555 ISBN-13(EAN): 9781627059558
Издательство: Mare Nostrum (Eurospan)
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Цена: 46200.00 T
Наличие на складе: Невозможна поставка.
Описание: Considers the crucial component needed to advance a single-object tracking system to a multi-object tracking system - data association. Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them.

Geometry and Complexity Theory

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.

Dynamic Fuzzy Machine Learning

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.

Julia for Data Science

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
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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.

Formation methods, models, and hardware implementation of pseudorandom number generators :

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
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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.

Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics

Автор: Thomas St?tzle; Mauro Birattari; Holger H. Hoos
Название: Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
ISBN: 364203750X ISBN-13(EAN): 9783642037504
Издательство: Springer
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Цена: 65210.00 T
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Описание: SLS methods include a wide spectrum of te- niques, ranging from constructive search procedures and iterative improvement algorithms to more complex SLS methods, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search, and variable neighborhood search.

Algorithms for Global Positioning

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.


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