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Zhechen Zhu Automatic Modulation Classification. Principles, Algorithms and Applications


Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind. Key Features: Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiers Lists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparison Gives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systems Includes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book

9298.83 РУБ

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Ludmila Kuncheva I. Combining Pattern Classifiers. Methods and Algorithms


A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.

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Alessandro Rozza | Assistant Professor | Parthenope ...

Alessandro Rozza’s research interests are in the machine learning and pattern recognition fields. From 2008 he is focusing his attention on the development of novel classifiers based on a new ...

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Since 2007 he has moved his research focus to a more theoretic field. Particularly, he has developed a set of novel classifiers based on a new estimation of the Fisher Subspace. These classifiers have shown their efficacy when applied to real problems. From 2009 to 2014 he has worked on Intrinsic Dimension estimation and Manifold Learning.

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In this book we present novel classifiers based on a new approach to estimate the Fisher Subspace. We face different classification tasks with the aim to stress our methods, to demonstrate their quality, and to show that these classifiers solve the following problems: 1. Manage unbalanced classes; 2

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Novel Fisher Subspace based Classifiers: Classifiers based on a New Approach to Estimate the Fisher Subspace and Their Applications by Alessandro Rozza (2014-05-03) | Alessandro Rozza | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

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