Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint (Record no. 76765)
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000 -LEADER | |
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fixed length control field | 03929nam a22006135i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-030-49395-0 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20220801214811.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 200701s2020 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783030493950 |
-- | 978-3-030-49395-0 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 621.382 |
100 1# - AUTHOR NAME | |
Author | Hinders, Mark K. |
245 10 - TITLE STATEMENT | |
Title | Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2020. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XIV, 346 p. 208 illus., 143 illus. in color. |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Background and history -- Intelligent structural health monitoring with ultrasonic lamb waves -- Automatic detection of flaws in recorded music -- Pocket depth determination with an ultrasonographic periodontal probe -- Spectral intermezzo: Spirit security systems -- Lamb wave tomographic rays in pipes -- Classification of RFID tags with wavelet fingerprinting -- Pattern classification for interpreting sensor data from a walking-speed robot -- Cranks and charlatans and deepfakes. |
520 ## - SUMMARY, ETC. | |
Summary, etc | This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify features for machine learning and pattern classification in time-domain signals. Whether for medical imaging or structural health monitoring, it develops analysis techniques and measurement technologies for the quantitative characterization of materials, tissues and structures by non-invasive means. Intelligent Feature Selection for Machine Learning using the Dynamic Wavelet Fingerprint begins by providing background information on machine learning and the wavelet fingerprint technique. It then progresses through six technical chapters, applying the methods discussed to particular real-world problems. Theses chapters are presented in such a way that they can be read on their own, depending on the reader’s area of interest, or read together to provide a comprehensive overview of the topic. Given its scope, the book will be of interest to practitioners, engineers and researchers seeking to leverage the latest advances in machine learning in order to develop solutions to practical problems in structural health monitoring, medical imaging, autonomous vehicles, wireless technology, and historical conservation. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-030-49395-0 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2020. |
336 ## - | |
-- | text |
-- | txt |
-- | rdacontent |
337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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347 ## - | |
-- | text file |
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-- | rda |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal processing. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Biomedical engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Materials—Analysis. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Control engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Robotics. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Automation. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer science. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal, Speech and Image Processing . |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Biomedical Engineering and Bioengineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Materials Characterization Technique. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Control, Robotics, Automation. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Science. |
912 ## - | |
-- | ZDB-2-ENG |
912 ## - | |
-- | ZDB-2-SXE |
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