@ARTICLE\{IMM2011-06098,
    author       = "T. Adali and D. J. Miller and K. Diamantaras and J. Larsen",
    title        = "Trends in Machine Learning for Signal Processing",
    year         = "2011",
    month        = "nov",
    keywords     = "machine learning, signal processing",
    pages        = "193 - 196",
    journal      = "{IEEE} Signal Processing Magazine",
    volume       = "28",
    editor       = "",
    number       = "11",
    publisher    = "{IEEE} Press",
    note         = "Video avialable at https://ieeetv.ieee.org/player/html/viewer\#icassp-2011-trends-in-machine-learning-for-signal-processing",
    url          = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=6021869&openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All%26queryText%3DJ.+Larsen+2011",
    abstract     = "By 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.",
    isbn_issn    = "{DOI} 10.1109/MSP.2011.942319"
}