Trends in Machine Learning for Signal Processing

Tülat Adali, David J. Miller, Konstantinos Diamantaras, Jan Larsen

AbstractBy putting the accent on learning from the data and the environment, the Machine Learning for SP (MLSP) Technical Committee (TC) provides the essential bridge between the machine learning and SP communities. While the emphasis in MLSP is on learning and data-driven approaches, SP defines the main applications of interest, and thus the constraints and requirements on solutions, which include computational efficiency, online adaptation, and learning with limited supervision/reference data.
Keywordsmachine learning, signal processing
TypeJournal paper [With referee]
JournalIEEE Signal Processing Magazine
Year2011    Month November    Vol. 28    No. 11    pp. 193 - 196
PublisherIEEE Press
ISBN / ISSNDOI 10.1109/MSP.2011.942319
NoteVideo avialable at https://ieeetv.ieee.org/player/html/viewer#icassp-2011-trends-in-machine-learning-for-signal-processing
Electronic version(s)[pdf]
Publication linkhttp://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=6021869&openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All%26queryText%3DJ.+Larsen+2011
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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