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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic (Paperback, 1st ed. 2018)
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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic (Paperback, 1st ed. 2018)
Series: SpringerBriefs in Computational Intelligence
Expected to ship within 10 - 15 working days
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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.
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