Selected Works III: Information Theory and the Theory of Algorithms, Kolmogorov Andrei N., Shiryaev Albert N.
Автор: Daniel J. Velleman Название: How to Prove It : A Structured Approach ISBN: 1108439535 ISBN-13(EAN): 9781108439534 Издательство: Cambridge Academ Рейтинг: Цена: 39070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Proofs play a central role in advanced mathematics and theoretical computer science, and this bestselling text`s third edition will help students transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs, with a new chapter on number theory and over 150 new exercises.
Автор: Cormen, Thomas H., E Название: Introduction to algorithms 3 ed. ISBN: 0262033844 ISBN-13(EAN): 9780262033848 Издательство: MIT Press Рейтинг: 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.
Автор: Agarwal, Dr Basant, Baka, Benjamin Название: Hands-On Data Structures and Algorithms with Python 2 ed ISBN: 1788995570 ISBN-13(EAN): 9781788995573 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data structures help us to organize and align the data in a very efficient way. This book will surely help you to learn important and essential data structures through Python implementation for better understanding of the concepts.
Автор: Emmanouil Amolochitis Название: Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining ISBN: 8793609647 ISBN-13(EAN): 9788793609648 Издательство: Taylor&Francis Рейтинг: Цена: 78590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of algorithms, a major part of the work presented in the book involves the development of new systems both for commercial as well as for academic use. In the first part of the book the author introduces a novel hierarchical heuristic scheme for re-ranking academic publications retrieved from standard digital libraries. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. In order to evaluate the performance of the introduced algorithms, a meta-search engine has been designed and developed that submits user queries to standard digital repositories of academic publications and re-ranks the top-n results using the introduced hierarchical heuristic scheme. In the second part of the book the design of novel recommendation algorithms with application in different types of e-commerce systems are described. The newly introduced algorithms are a part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. The initial version of the system uses a novel hybrid recommender (user, item and content based) and provides daily recommendations to all active subscribers of the provider (currently more than 30,000). The recommenders that we are presenting are hybrid by nature, using an ensemble configuration of different content, user as well as item-based recommenders in order to provide more accurate recommendation results.The final part of the book presents the design of a quantitative association rule mining algorithm. Quantitative association rules refer to a special type of association rules of the form that antecedent implies consequent consisting of a set of numerical or quantitative attributes. The introduced mining algorithm processes a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. The generated rules show strong relationships that exist between the consequent and the antecedent of each rule, representing different items that have been consumed at specific price levels. This research book will be of appeal to researchers, graduate students, professionals, engineers and computer programmers.
Автор: Urban Larsson Название: Games of No Chance 5 ISBN: 1108485804 ISBN-13(EAN): 9781108485807 Издательство: Cambridge Academ Рейтинг: Цена: 142560.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book surveys the state-of-the-art in combinatorial game theory, that is games not involving chance or hidden information. Topics include scoring, bidding chess, Wythoff Nim, misere play, partizan bidding, loopy games, and placement games, along with a survey of temperature theory by Elwyn Berlekamp and a list of unsolved problems.
Автор: Bhattacharya, Bhargab B. Sur-kolay, Susmita Nandy, Название: Algorithms, architectures and information systems security ISBN: 9812836233 ISBN-13(EAN): 9789812836236 Издательство: World Scientific Publishing Рейтинг: Цена: 139390.00 T Наличие на складе: Невозможна поставка. Описание: Addresses various geometric problems and related algorithms. This title focuses on various optimization issues in VLSI design and text architectures, and in wireless networks. It comprises scholarly articles on information systems security covering privacy issues, access control, enterprise and network security, and digital image forensics.
Автор: Downey Allen B. Название: Think Data Structures: Algorithms and Information Retrieval in Java ISBN: 1491972394 ISBN-13(EAN): 9781491972397 Издательство: Wiley Рейтинг: Цена: 33780.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: If you`re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering-data structures and algorithms-in a way that`s clearer, more concise, and more engaging than other materials.
Автор: Manuel Grana; Richard J. Duro; Alicia d`Anjou; Pau Название: Information Processing with Evolutionary Algorithms ISBN: 184996937X ISBN-13(EAN): 9781849969376 Издательство: Springer Рейтинг: Цена: 144410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides a broad sample of current information processing applicationsIncludes examples of successful applications that will encourage practitioners to apply the techniques described in the book to real-life problems
Автор: Ray Название: Numerical Analysis with Algorithms and Programming ISBN: 1498741746 ISBN-13(EAN): 9781498741743 Издательство: Taylor&Francis Рейтинг: Цена: 132710.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Numerical Analysis with Algorithms and Programming is the first comprehensive textbook to provide detailed coverage of numerical methods, their algorithms, and corresponding computer programs. It presents many techniques for the efficient numerical solution of problems in science and engineering.
Along with numerous worked-out examples, end-of-chapter exercises, and Mathematica(R) programs, the book includes the standard algorithms for numerical computation:
Root finding for nonlinear equations
Interpolation and approximation of functions by simpler computational building blocks, such as polynomials and splines
The solution of systems of linear equations and triangularization
Approximation of functions and least square approximation
Numerical differentiation and divided differences
Numerical quadrature and integration
Numerical solutions of ordinary differential equations (ODEs) and boundary value problems
Numerical solution of partial differential equations (PDEs)
The text develops students' understanding of the construction of numerical algorithms and the applicability of the methods. By thoroughly studying the algorithms, students will discover how various methods provide accuracy, efficiency, scalability, and stability for large-scale systems.
Название: Introduction to Scheduling ISBN: 1138117722 ISBN-13(EAN): 9781138117723 Издательство: Taylor&Francis Рейтинг: Цена: 74510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material.
The book first classifies scheduling problems and their complexity and then presents examples that demonstrate successful techniques for the design of efficient approximation algorithms. It also discusses classical problems, such as the famous makespan minimization problem, as well as more recent advances, such as energy-efficient scheduling algorithms. After focusing on job scheduling problems that encompass independent and possibly parallel jobs, the text moves on to a practical application of cyclic scheduling for the synthesis of embedded systems. It also proves that efficient schedules can be derived in the context of steady-state scheduling. Subsequent chapters discuss scheduling large and computer-intensive applications on parallel resources, illustrate different approaches of multi-objective scheduling, and show how to compare the performance of stochastic task-resource systems. The final chapter assesses the impact of platform models on scheduling techniques.
From the basics to advanced topics and platform models, this volume provides a thorough introduction to the field. It reviews classical methods, explores more contemporary models, and shows how the techniques and algorithms are used in practice.
Автор: Xia Lirong Название: Learning and Decision-Making from Rank Data ISBN: 1681734400 ISBN-13(EAN): 9781681734408 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 61910.00 T Наличие на складе: Невозможна поставка. Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.
Автор: Xia Lirong Название: Learning and Decision-Making from Rank Data ISBN: 1681734427 ISBN-13(EAN): 9781681734422 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 82230.00 T Наличие на складе: Невозможна поставка. Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.
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