Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Februar 2018
Genre:	Naturwissensch., Medizin, Technik
	Artificial Intelligence / C / Computational Intelligence / data classification / engineering / Learning Vector Quantization
ISBN:	9783319737720
EAN-Code:	9783319737720
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Serie:	SpringerBriefs in Applied Sciences and Technology SpringerBriefs in Computational Intelligence
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	194 gr
Seiten:	73
Illustration:	VIII, 73 p. 22 illus., 12 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.