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Machine Learning Techniques for Time Series Classification

Impresion
EUR 49,90

E-Book
EUR 34,90

Machine Learning Techniques for Time Series Classification (Volumen 2) (Tienda española)

Michael Botsch (Autor)

Previo

Lectura de prueba, PDF (200 KB)
Indice, PDF (51 KB)

ISBN-13 (Impresion) 9783736978133
ISBN-13 (E-Book) 9783736968134
Idioma Inglés
Numero de paginas 216
Laminacion de la cubierta mate
Edicion 2.
Serie Künstliche Intelligenz & Digitalisierung
Volumen 2
Lugar de publicacion Göttingen
Lugar de la disertacion TU München
Fecha de publicacion 23.06.2023
Clasificacion simple Tesis doctoral
Area Ingeniería eléctrica
Palabras claves time series, car crashes, machine learning, airbags, protection of passengers
Descripcion

Classification of time series is an important task in various fields, e.g., medicine, finance, and industrial applications. This work discusses strong temporal classification using machine learning techniques. Here, two problems must be solved: the detection of those time instances when the class labels change and the correct assignment of the labels. For this purpose the scenario-based random forest algorithm and a segment and label approach are introduced. The latter is realized with either the augmented dynamic time warping similarity measure or with interpretable generalized radial basis function classifiers.
The main application presented in this work is the detection and categorization of car crashes using machine learning. Depending on the crash severity different safety systems, e.g., belt tensioners or airbags must be deployed at time instances when the best-possible protection of passengers is assured.