000 03156nam a22005655i 4500
001 978-3-319-98675-3
003 DE-He213
005 20220801214930.0
007 cr nn 008mamaa
008 180823s2019 sz | s |||| 0|eng d
020 _a9783319986753
_9978-3-319-98675-3
024 7 _a10.1007/978-3-319-98675-3
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC059000
_2bisacsh
072 7 _aMQW
_2thema
082 0 4 _a610.28
_223
100 1 _aPham, Thuy T.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_941329
245 1 0 _aApplying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings
_h[electronic resource] /
_cby Thuy T. Pham.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXV, 107 p. 35 illus., 32 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
505 0 _aIntroduction -- Background -- Algorithms -- Point Anomaly Detection: Application to Freezing of Gait Monitoring -- Collective Anomaly Detection: Application to Respiratory Artefact Removals -- Spike Sorting: Application to Motor Unit Action Potential Discrimination -- Conclusion .
520 _aThis book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
650 0 _aBiomedical engineering.
_93292
650 0 _aData mining.
_93907
650 0 _aComputational intelligence.
_97716
650 0 _aBioinformatics.
_99561
650 1 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aData Mining and Knowledge Discovery.
_941330
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aBioinformatics.
_99561
710 2 _aSpringerLink (Online service)
_941331
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319986746
776 0 8 _iPrinted edition:
_z9783319986760
776 0 8 _iPrinted edition:
_z9783030075187
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
_941332
856 4 0 _uhttps://doi.org/10.1007/978-3-319-98675-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c76916
_d76916