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  • Multiple Instance Multiple Label Learning with Limited Supervision

    Creator:
    Nguyen, Tam
    Abstract:
    In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...
    Resource Type:
    Dissertation
    Full Text:
    (a) n for w = 5/ √ 2.[1, 1]> and β = 1, (b) ‖w‖ for β = 1 and n ∈ [20, 100, 1000], and (c) t (β = σ

  • Multiple Instance Learning for Histopathological Image Classification

    Creator:
    Lai, Phung
    Abstract:
    In histopathological image analysis, image classification as well as pattern detection play a crucial role in the diagnosis and treatment process since the goal is to not only differentiate cancer types but also identify cancerous manifestations. Fully supervised learning strategies tend to address these problems using manually annotated cancerous regions...
    Resource Type:
    Capstone Project
    Full Text:
    find a transformation y = T (x) in which both source and output images are matched into the histogram

  • Weak-supervision : Probabilistic Models and Inference

    Creator:
    Pham, Anh
    Abstract:
    In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . 41 1A. T. Pham, Raviv Raich, and Xiaoli Z. Fern, Dynamic Programming for Instance Annotation in