New Classification Method Based by Amezcua Jonathan (15 results)
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Published by Springer 2018-02, 2018
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Published by Springer-Verlag New York Inc, 2018
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Published by Springer, Berlin, Springer, 2018
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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 afore…mentioned 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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Taschenbuch. Condition: Neu. New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic | Jonathan Amezcua (u. a.) | Taschenbuch | viii | Englisch | 2018 | Springer | EAN 9783319737720 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, jue…rgen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Published by Berlin Springer International Publishing Springer Feb 2018, 2018
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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 hybridizat…ion 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.
Language: English
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Condition: New. Print on Demand pp. 81.
Language: English
Published by Springer, 2018
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Condition: New. PRINT ON DEMAND pp. 81.
