| 000 | 03400nam a2200409 i 4500 | ||
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_c121788 _d121788 _x1 |
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| 001 | 121788 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230102114149.0 | ||
| 006 | a||||fo|||| 00| 0 | ||
| 007 | cr nn nnnaamaa | ||
| 008 | 200701s2020 gw a o |||| 0|eng d | ||
| 020 | _a9783030493950 | ||
| 024 | 7 |
_a10.1007/978-3-030-49395-0 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
||
| 050 | 4 |
_aQ325.5 _b2020 EB |
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| 100 | 1 |
_aHinders, Mark K., _eautor _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/69084964 _9675585 _d1963- |
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| 245 | 1 | 0 |
_aIntelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint _cby Mark K. Hinders |
| 250 | _aFirst edition | ||
| 264 | 1 |
_aCham _bSpringer International Publishing _c2020 |
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| 300 |
_a1 recurso en línea (XIV, 346 páginas) _b208 ilustraciones, 143 ilustraciones a color |
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| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
||
| 347 |
_aarchivo de texto _bPDF |
||
| 490 | 0 | _aEngineering (SpringerNature-11647) | |
| 490 | 0 | _aEngineering (R0) (SpringerNature-43712) | |
| 505 | 0 | _aBackground 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 | 3 | _aThis 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. | |
| 988 | _aSpringer_Engineering_03082020 | ||
| 650 | 7 |
_2embne _aAprendizaje automático _9166090 |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783030493943 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030493967 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030493974 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-49395-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b08/2020 _dz _ek _zSI |
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