Introduction to the Design and Analysis of Algorithms PEARSON Education Limited

Książki / Literatura obcojęzyczna

Based on a new classification of algorithm design techniques and a clear delineation of analysis methods, Introduction to the Design and Analysis of Algorithms presents the subject in a coherent and innovative manner. Written in a student-friendly style, the book emphasizes the understanding of ideas over excessively formal treatment while thoroughly covering the material required in an introductory algorithms course. Popular puzzles are used to motivate students' interest and strengthen their skills in algorithmic problem solving. Other learning-enhancement features include chapter summaries, hints to the exercises, and a detailed solution manual.

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Algorithms: Design And Analysis

Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Mathematical theory of computation>Maths for comput...

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Design And Analysis Of Algorithms

Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science

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Design And Analysis Of Algorithms

Książki Obcojęzyczne>Angielskie>Computing & information technology>Information technology: general issues

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Analysis And Design Of Handoff Algorithms

Książki Obcojęzyczne>Angielskie>Technology, engineering, agriculture>Electronics & communications engineering>Electronics engineering

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Design And Analysis Of Randomized Algorithms

Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer programming / software development>Algorithms & data structure...

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Introduction to the Design Analysis of Algorithms

Książki

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Analysis and Design of Algorithms

Książki

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Data Structures and Algorithms in Python John Wiley & Sons Inc

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Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website.Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects.

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Machine Learning Algorithms Springer, Berlin

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Machine learning is widely used in signal processing which is demonstrated in this book. The success of machine learning in signal processing relies heavily on the quality of the data which is also demonstrated in this book. However, the diverse data sources make it harder to get very high-quality data. What makes it worse is that there might be a malicious adversary, who can deliberately modify the data or add poisoning data to corrupt the learning system. This imposes a significant threat to machine learning in signal processing, for example, in wireless communication, array signal processing, and image signal processing. Hence, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors examine the adversarial robustness of three commonly used machine learning algorithms in signal processing: linear regression, LASSO-based feature selection, and principal component analysis (PCA). Based on the theoretical analysis for this book, the authors also carry out adversarial attacks on several signal processing problems, such as feature selection, array signal processing, principal component analysis, wireless sensor networks and more. The first part of this book studies the adversarial robustness of linear regression. The authors assume there is an adversary in the linear regression system and it tries to suppress or promote one of the regression coefficients. To obtain this goal, the adversary adds poisoning data samples or directly modifies the feature matrix of the original data. The authors derive the optimal poisoning data sample and propose an alternating optimization method to design the optimal feature modification. It also demonstrate the effectiveness of the attack against a wireless distributed learning system. The second part of this book extends the linear regression to LASSO-based feature selection and studies the best strategy to modify the feature matrix or response values to mislead the learning system to select the wrong features. The authors formulate this problem as a bi-level optimization problem and use a smooth approximation of the norm function to attain the gradient of our objective function. With the gradient information, the authors employ the projected gradient method to find the optimal attacks. It also illustrates how this attack influences array signal processing and weather data analysis. The last section of this book considers the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm and derive the optimal attack strategy to modify the original data to maximize the subspace distance between the original one and the one after modification. This book also conducts an attack on a principal regression problem and demonstrate its impacts on the subspace and the regression result. This book targets researchers working in machine Learning, electronic information and Information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning and technical consultants working on the security and robustness of machine learning will also want to purchase this book as a reference guide.

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Machine Vision Algorithms in Java Springer London Ltd

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Machine Vision Algorithms in Java provides a comprehensive introduction to the algorithms and techniques associated with machine vision systems. The Java programming language is also introduced, with particular reference to its imaging capabilities. The book contains explanations of key machine vision techniques and algorithms, along with the associated Java source code.Special features include: - A complete self-contained treatment of the topics and techniques essential to the understanding and implementation of machine vision.- An introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities.- Java source code for a wide range of practical image processing and analysis functions.- Readers will be given the opportunity to download a fully functional Java-based visual programming environment for machine vision, available via the WWW. This contains over 200 image processing, manipulation and analysis functions and will enable users to implement many of the ideas covered in this book. - Details relating to the design of a Java-based visual programming environment for machine vision.- An introduction to the Java 2D imaging and Java Advanced Imaging (JAI) APIs- A wide range of illustrative examples.- Practical treatment of the subject matter. This book is aimed at senior undergraduate and postgraduate students in engineering and computer science as well as practitioners in machine vision who may wish to update or expand their knowledge of the subject. The techniques and algorithms of machine vision are expounded in a way that will be understood not only by specialists but also by those who are less familiar with the topic.

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Data Structures and Algorithms in C++ 2e (WSE) John Wiley & Sons Inc

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An updated, innovative approach to data structures and algorithms Written by an author team of experts in their fields, this authoritative guide demystifies even the most difficult mathematical concepts so that you can gain a clear understanding of data structures and algorithms in C++. The unparalleled author team incorporates the object-oriented design paradigm using C++ as the implementation language, while also providing intuition and analysis of fundamental algorithms. Offers a unique multimedia format for learning the fundamentals of data structures and algorithms Allows you to visualize key analytic concepts, learn about the most recent insights in the field, and do data structure design Provides clear approaches for developing programs Features a clear, easy-to-understand writing style that breaks down even the most difficult mathematical concepts Building on the success of the first edition, this new version offers you an innovative approach to fundamental data structures and algorithms.

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Data Structures and Algorithms in Java 6E John Wiley & Sons Inc

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The design and analysis of efficient data structures has long been recognized as a key component of the Computer Science curriculum. Goodrich, Tomassia and Goldwasser's approach to this classic topic is based on the object-oriented paradigm as the framework of choice for the design of data structures.

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Data Structures & Algorithms in Java 6e International Student Version John Wiley & Sons Inc

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This sixth edition incorporates the object-oriented design paradigm, using Java as the implementation language, while also providing intuition and analysis of fundamental data algorithms.

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Python Algorithms APress

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Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. §The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others. §New in this second edition: Basic algorithmic models for parallelism, appropriate for big data applications and batch processing §Various forms of "black-box solutions" available to Python programmers to solve algorithmic problems§Heuristic optimizations such as those used for simulated annealing and bioinformatic programming

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