Synopsis
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.
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Machine Learning Techniques for Time Series Classification
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Machine Learning Techniques for Time Series Classification
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Cuvillier, Cuvillier Jun 2023, 2023
ISBN 10: 3736978138
ISBN 13: 9783736978133
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Machine Learning Techniques for Time Series Classification
Published by
Cuvillier, Cuvillier Jun 2023, 2023
ISBN 10: 3736978138
ISBN 13: 9783736978133
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 216 pp. Englisch. Seller Inventory # 9783736978133
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Machine Learning Techniques for Time Series Classification
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783736978133
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Machine Learning Techniques for Time Series Classification
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Taschenbuch. Condition: Neu. Machine Learning Techniques for Time Series Classification | Michael Botsch | Taschenbuch | Künstliche Intelligenz & Digitalisierung 2 | Englisch | 2023 | Cuvillier | EAN 9783736978133 | Verantwortliche Person für die EU: Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen, info[at]cuvillier[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 133712093