Trends in machine learning for signal processing [In the Spotlight]

Tülay Adali, David J. Miller, Konstantinos I. Diamantaras, Jan Larsen

    Research output: Contribution to journalArticle

    6 Scopus citations

    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.

    Original languageEnglish (US)
    Article number6021869
    Pages (from-to)193-196
    Number of pages4
    JournalIEEE Signal Processing Magazine
    Volume28
    Issue number6
    DOIs
    StatePublished - Nov 2011

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics

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