Deep learning classifiers with memristive networks., Alex Pappachen James

Автор: Ludmila I. Kuncheva
Название: Combining Pattern Classifiers: Methods and Algorithms
ISBN: 1118315235 ISBN-13(EAN): 9781118315231
Издательство: Wiley
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Цена: 15674.00 р.
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Описание: Combined classifiers, which are central to the ubiquitous performance of pattern recognition and machine learning, are generally considered more accurate than single classifiers.

Автор: Jaume Bacardit; Ester Bernad?-Mansilla; Martin V.
Название: Learning Classifier Systems
ISBN: 3540881379 ISBN-13(EAN): 9783540881377
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Covers post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - hosted by the Genetic and Evolutionary Computation Conference, GECCO. This book features sections on knowledge representation, and analysis of the system.

Автор: Michal Wozniak
Название: Hybrid Classifiers
ISBN: 3642409962 ISBN-13(EAN): 9783642409967
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book details how hybridization can help improve the quality of computer classification systems. It introduces the different levels of hybridization and illuminates common problems faced when dealing with such projects.

Автор: Larry Bull
Название: Applications of Learning Classifier Systems
ISBN: 3642535593 ISBN-13(EAN): 9783642535598
Издательство: Springer
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Цена: 20962.00 р.
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Описание: The field called Learning Classifier Systems is populated with romantics. The system embracing such a rule "popu- lation" would explore its available actions and responses, rewarding and rating the active rules accordingly.

Автор: Josef Kittler; Fabio Roli
Название: Multiple Classifier Systems
ISBN: 3540677046 ISBN-13(EAN): 9783540677048
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This text constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000. The 33 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book.

Автор: Nikunj C. Oza; Robi Polikar; Josef Kittler; Fabio
Название: Multiple Classifier Systems
ISBN: 3540263063 ISBN-13(EAN): 9783540263067
Издательство: Springer
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Цена: 13275.00 р.
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Описание: Constitutes the refereed proceedings of the 6th International Workshop on Multiple Classifier Systems, MCS 2005. This book contains papers that are organized in topical sections on boosting, combination methods, performance analysis, and applications. They exemplify the advances in the theory and applications of multiple classifier systems.

Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils
Название: Learning Classifier Systems
ISBN: 3540677291 ISBN-13(EAN): 9783540677291
Издательство: Springer
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Цена: 11179.00 р.
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Описание: This text provides a survey of the current state-of-the-art of LCS and highlights some of the most promising research directions. The first part presents various views on what learning classifier systems are. The second part is devoted to advanced topics of current interest.

Автор: Sundarapandian Vaidyanathan; Christos Volos
Название: Advances in Memristors, Memristive Devices and Systems
ISBN: 3319517236 ISBN-13(EAN): 9783319517230
Издательство: Springer
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Цена: 30745.00 р.
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Описание:

Chapter 1 Memristor Emulators A Note on Modeling.- Chapter 2 A Simple Oscillator using Memristor.- Chapter 3 A Hyperjerk Memristive System with Hidden Attractors.- Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application.- Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based.- Chapter 6 Modern System Design using Memristors.- Chapter 7 RF/Microwave Applications of Memristors.- Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits.- Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors.- Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki's System.

Автор: Sundarapandian Vaidyanathan; Christos Volos
Название: Advances in Memristors, Memristive Devices and Systems
ISBN: 3319847279 ISBN-13(EAN): 9783319847276
Издательство: Springer
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Цена: 32142.00 р.
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Описание:

Chapter 1 Memristor Emulators A Note on Modeling.- Chapter 2 A Simple Oscillator using Memristor.- Chapter 3 A Hyperjerk Memristive System with Hidden Attractors.- Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application.- Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based.- Chapter 6 Modern System Design using Memristors.- Chapter 7 RF/Microwave Applications of Memristors.- Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits.- Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors.- Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki's System.

Автор: Michal Wozniak
Название: Hybrid Classifiers
ISBN: 3662523043 ISBN-13(EAN): 9783662523049
Издательство: Springer
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Цена: 15672.00 р.
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Описание: This book details how hybridization can help improve the quality of computer classification systems. It introduces the different levels of hybridization and illuminates common problems faced when dealing with such projects.

Автор: Sarunas Raudys
Название: Statistical and Neural Classifiers
ISBN: 1447110714 ISBN-13(EAN): 9781447110712
Издательство: Springer
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Цена: 19564.00 р.
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Описание: In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used..

Автор: Zhi-Hua Zhou; Fabio Roli; Josef Kittler
Название: Multiple Classifier Systems
ISBN: 3642380662 ISBN-13(EAN): 9783642380662
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the refereed proceedings of the 11th International Workshop on Multiple Classifier Systems, MCS 2013, held in Nanjing, China, in May 2013. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics.