Deep learning classifiers with memristive networks., Alex Pappachen James

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: 130430.00 T
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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: 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: 88500.00 T
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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: 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: 204970.00 T
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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: 3642409962 ISBN-13(EAN): 9783642409967
Издательство: Springer
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Цена: 130430.00 T
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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: Jurgen Beyerer, Matthias Richter, Matthias Nagel
Название: Pattern Recognition: Introduction, Features, Classifiers and Principles
ISBN: 3110537931 ISBN-13(EAN): 9783110537932
Издательство: Walter de Gruyter
Цена: 86720.00 T
Наличие на складе: Невозможна поставка.
Описание: The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features, their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book is to clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors’ point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book. Mathematical methods explained thoroughly Extremely practical approach with many examples Based on over ten years lecture at Karlsruhe Institute of Technology For students but also for practitioners

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: 46570.00 T
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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
Название: Applications of Learning Classifier Systems
ISBN: 3642535593 ISBN-13(EAN): 9783642535598
Издательство: Springer
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Цена: 139750.00 T
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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: 88500.00 T
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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: 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: 93160.00 T
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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: 214280.00 T
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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: 104480.00 T
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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: Ludmila I. Kuncheva
Название: Combining Pattern Classifiers: Methods and Algorithms
ISBN: 1118315235 ISBN-13(EAN): 9781118315231
Издательство: Wiley
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Цена: 104490.00 T
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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.