Search Results - "Difference Convex Programming"

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  1. 1

    Joint Resource Allocation for Full-Duplex Ambient Backscatter Communication: A Difference Convex Algorithm by Madavani, Fatemeh Kaveh, Soleimanpour-Moghadam, Mohadeseh, Talebi, Siamak, Chatzinotas, Symeon, Ottersten, Bjorn

    ISSN: 1536-1276, 1558-2248, 1558-2248
    Published: New York IEEE 01.10.2022
    “…Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless…”
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    Journal Article
  2. 2

    On constrained and regularized high-dimensional regression by Shen, Xiaotong, Pan, Wei, Zhu, Yunzhang, Zhou, Hui

    ISSN: 0020-3157, 1572-9052
    Published: Tokyo Springer Japan 01.10.2013
    “…High-dimensional feature selection has become increasingly crucial for seeking parsimonious models in estimation. For selection consistency, we derive one…”
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  3. 3

    On optimization of cooperative MIMO for underlaid secrecy Industrial Internet of Things by Wang, Xinyao, Bao, Xuyan, Huang, Yuzhen, Zheng, Zhong, Fei, Zesong

    ISSN: 2095-9184, 2095-9230
    Published: Hangzhou Zhejiang University Press 01.02.2023
    “…In this paper, physical layer security techniques are investigated for cooperative multi-input multi-output (C-MIMO), which operates as an underlaid cognitive…”
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  4. 4

    High-dimensional sign-constrained feature selection and grouping by Qin, Shanshan, Ding, Hao, Wu, Yuehua, Liu, Feng

    ISSN: 0020-3157, 1572-9052
    Published: Tokyo Springer Japan 01.08.2021
    “…In this paper, we propose a non-negative feature selection/feature grouping (nnFSG) method for general sign-constrained high-dimensional regression problems…”
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    Journal Article
  5. 5

    On Efficient Large Margin Semisupervised Learning: Method and Theory by Wang, Junhui, Shen, Xiaotong, Pan, Wei

    ISSN: 1532-4435
    Published: United States 01.03.2009
    Published in Journal of machine learning research (01.03.2009)
    “…In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great…”
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    Journal Article