@MISC\{IMM2011-06208,
    author       = "T. Adali and K. Diamantaras and J. Larsen",
    title        = "Trends in Machine Learning for Signal Processing",
    year         = "2011",
    month        = "may",
    keywords     = "trends, machine learning, signal processing, icassp 2011",
    publisher    = "{IEEE} Press",
    address      = "",
    note         = "Companion paper at http://www.imm.dtu.dk/pubdb/p.php?6098",
    url          = "http://https://ieeetv.ieee.org/player/html/viewer#icassp-2011-trends-in-machine-learning-for-signal-processing",
    abstract     = "By putting the accent on {''}learning{''} from the data and the environment, the {MLSP} {TC} provides the essential bridge between the machine learning and signal processing communities. {MLSP} techniques have always been attractive solutions for traditional signal processing applications such as pattern recognition, speech, audio, and video processing. More importantly, owing to their polyvalent nature, these methods are also primary candidates for a new wave of emerging applications such as brain-computer interface, multimodal data fusion and processing, behavior and emotion recognition, and learning in environments such as social networks. At this session, we will discuss the role {MLSP} plays in such emerging applications as well as major paradigm shifts in learning as demonstrated by cognitive systems. We shall also explore what these paradigm shifts offer for the signal processing community."
}