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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic (Paperback)
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologyLanguage: English
Published by Springer International Publishing AG, Cham, 2018
ISBN 10: 3319737724 ISBN 13: 9783319737720
First Edition
Paperback. Condition: new. Paperback. In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Both datasets show interesting features that makes them interesting for testing new classification methods. In this book a new model for data classification was developed. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: As New. Unread book in perfect condition.
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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic (SpringerBriefs in Applied Sciences and Technology)
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologySeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 47.99
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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New Classification Method Based on Modular Neural Networks With the Lvq Algorithm and Type-2 Fuzzy Logic
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologySeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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New Classification Method Based on Modular Neural Networks With the Lvq Algorithm and Type-2 Fuzzy Logic
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologySeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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New Classification Method Based on Modular Neural Networks With the Lvq Algorithm and Type-2 Fuzzy Logic
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologyLanguage: English
Published by Springer-Verlag New York Inc, 2018
ISBN 10: 3319737724 ISBN 13: 9783319737720
Paperback. Condition: Brand New. 84 pages. 9.25x6.10x0.47 inches. In Stock.
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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologyLanguage: English
Published by Springer International Publishing, 2018
ISBN 10: 3319737724 ISBN 13: 9783319737720
Kartoniert / Broschiert. Condition: New.
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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologyLanguage: English
Published by Springer, Berlin, Springer International Publishing, Springer, 2018
ISBN 10: 3319737724 ISBN 13: 9783319737720
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Both datasets show interesting features that makes them interesting for testing new classification methods.
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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic (Paperback)
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologyLanguage: English
Published by Springer International Publishing AG, Cham, 2018
ISBN 10: 3319737724 ISBN 13: 9783319737720
First Edition
Paperback. Condition: new. Paperback. In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Both datasets show interesting features that makes them interesting for testing new classification methods. In this book a new model for data classification was developed. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types ofsoil. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic
Book 74 of 322: SpringerBriefs in Applied Sciences and TechnologyLanguage: English
Published by Berlin Springer International Publishing Springer Feb 2018, 2018
ISBN 10: 3319737724 ISBN 13: 9783319737720
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods. 73 pp. Englisch.
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