Deep learning classifiers with memristive networks., Alex Pappachen James

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: J?n Atli Benediktsson; Josef Kittler; Fabio Roli
Название: Multiple Classifier Systems
ISBN: 3642023258 ISBN-13(EAN): 9783642023255
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Constitutes the refereed proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009, held in Reykjavik, Iceland, in June 2009. This work contains papers that are organized in topical sections on ECOC boosting and bagging, MCS in remote sensing, unbalanced data and decision templates, concept drift and SVM ensembles.

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: 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: 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.

Автор: Larry Bull; Ester Bernad?-Mansilla; John Holmes
Название: Learning Classifier Systems in Data Mining
ISBN: 3642097758 ISBN-13(EAN): 9783642097751
Издательство: Springer
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Цена: 19589.00 р.
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Описание: The ability of Learning Classifier Systems (LCS) to solve complex real-world problems is becoming clear. This book brings together work by a number of individuals who demonstrate the good performance of LCS in a variety of domains.

Автор: Josef Kittler; Fabio Roli
Название: Multiple Classifier Systems
ISBN: 3540422846 ISBN-13(EAN): 9783540422846
Издательство: Springer
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Цена: 12157.00 р.
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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: Kovacs, Tim
Название: Strength or accuracy: credit assignment in learning classifier systems
ISBN: 1447110587 ISBN-13(EAN): 9781447110583
Издательство: Springer
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Цена: 19564.00 р.
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Описание: Classifier systems are an intriguing approach to a broad range of machine learning problems, based on automated generation and evaluation of condi tion/action rules.

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: 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.