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Synopsis
Dynamic Time Warping (DTW) is a well-known technique used to determine alignment between two temporal sequences. DTW has been used in wide range of applications and it can be applied on any data which can be represented as linear sequence. Existing DTW libraries have out dated implementation of core DTW algorithm, which result in low performance or are inapplicable for big sequences. The aim of this book is to present, detailed concept of DTW along with a comprehensive DTW library, encapsulating the implementation of DTW variants with recently proposed efficient algorithms. Further down the road, in this book I presented a python module for computing and visualizing DTW alignment: DTWPy. DTWPy has implementation of classical DTW and recently proposed performance efficient algorithms i.e. fastDTW and DDTW. Correctness of the algorithms is verified by comparing it with existing R implementation. This book gives the basic idea to the readers about what DTW is and DTWPy have the most comprehensive implementation of DTW algorithms present in literature to date, and is applicable on large temporal sequence.
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Comprehensive Guide to Dynamic Time Warping in Python
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Comprehensive Guide to Dynamic Time Warping in Python
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Dynamic Time Warping (DTW) is a well-known technique used to determine alignment between two temporal sequences. DTW has been used in wide range of applications and it can be applied on any data which can be represented as linear sequence. Existing DTW libraries have out dated implementation of core DTW algorithm, which result in low performance or are inapplicable for big sequences. The aim of this book is to present, detailed concept of DTW along with a comprehensive DTW library, encapsulating the implementation of DTW variants with recently proposed efficient algorithms. Further down the road, in this book I presented a python module for computing and visualizing DTW alignment: DTWPy. DTWPy has implementation of classical DTW and recently proposed performance efficient algorithms i.e. fastDTW and DDTW. Correctness of the algorithms is verified by comparing it with existing R implementation. This book gives the basic idea to the readers about what DTW is and DTWPy have the most comprehensive implementation of DTW algorithms present in literature to date, and is applicable on large temporal sequence. 60 pp. Englisch. Seller Inventory # 9786139858026
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Comprehensive Guide to Dynamic Time Warping in Python
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Comprehensive Guide to Dynamic Time Warping in Python
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Comprehensive Guide to Dynamic Time Warping in Python
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Dynamic Time Warping (DTW) is a well-known technique used to determine alignment between two temporal sequences. DTW has been used in wide range of applications and it can be applied on any data which can be represented as linear sequence. Existing DTW libraries have out dated implementation of core DTW algorithm, which result in low performance or are inapplicable for big sequences. The aim of this book is to present, detailed concept of DTW along with a comprehensive DTW library, encapsulating the implementation of DTW variants with recently proposed efficient algorithms. Further down the road, in this book I presented a python module for computing and visualizing DTW alignment: DTWPy. DTWPy has implementation of classical DTW and recently proposed performance efficient algorithms i.e. fastDTW and DDTW. Correctness of the algorithms is verified by comparing it with existing R implementation. This book gives the basic idea to the readers about what DTW is and DTWPy have the most comprehensive implementation of DTW algorithms present in literature to date, and is applicable on large temporal sequence. Seller Inventory # 9786139858026
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Comprehensive Guide to Dynamic Time Warping in Python
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Comprehensive Guide to Dynamic Time Warping in Python
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gulzar Hafiz MuhammadHafiz Muhammad Gulzar holds a Master of Science degree in Computer Science from University of Stavanger, Norway. Mr Gulzar has 5+ year of software development experience which includes Python.Dynamic Time War. Seller Inventory # 385874577
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Comprehensive Guide to Dynamic Time Warping in Python
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LAP LAMBERT Academic Publishing Jul 2018, 2018
ISBN 10: 613985802X
ISBN 13: 9786139858026
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Dynamic Time Warping (DTW) is a well-known technique used to determine alignment between two temporal sequences. DTW has been used in wide range of applications and it can be applied on any data which can be represented as linear sequence. Existing DTW libraries have out dated implementation of core DTW algorithm, which result in low performance or are inapplicable for big sequences. The aim of this book is to present, detailed concept of DTW along with a comprehensive DTW library, encapsulating the implementation of DTW variants with recently proposed efficient algorithms. Further down the road, in this book I presented a python module for computing and visualizing DTW alignment: DTWPy. DTWPy has implementation of classical DTW and recently proposed performance efficient algorithms i.e. fastDTW and DDTW. Correctness of the algorithms is verified by comparing it with existing R implementation. This book gives the basic idea to the readers about what DTW is and DTWPy have the most comprehensive implementation of DTW algorithms present in literature to date, and is applicable on large temporal sequence.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Seller Inventory # 9786139858026
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Comprehensive Guide to Dynamic Time Warping in Python
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Taschenbuch. Condition: Neu. Comprehensive Guide to Dynamic Time Warping in Python | Hafiz Muhammad Gulzar | Taschenbuch | 60 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139858026 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 114175384