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Machine Learning Techniques for Time Series Classification PDF

217 Pages·2023·12 MB·English
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by Michael-Felix Botsch| 2023| 217 pages| 12| English

About Machine Learning Techniques for Time Series Classification

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

Detailed Information

Author:Michael-Felix Botsch
Publication Year:2023
ISBN:9783736978133
Pages:217
Language:English
File Size:12
Format:PDF
Price:FREE
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