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Filtering by: Creator Meigs, Andrew J. Remove constraint Creator: Meigs, Andrew J. Committee Member Wong, Weng-Keen Remove constraint Committee Member: Wong, Weng-Keen Creator Hao, Guohua Remove constraint Creator: Hao, Guohua

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  • Revisiting output coding for sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this...
    Resource Type:
    Masters Thesis
    Full Text:
    unknown 9 distribution P (x, y), that is, ∆m(xi, yi) = [ − ∂ ∂Ψ(xi, yi) ∑ j `(xj, yj, Ψ(xj, yj

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
    Resource Type:
    Dissertation
    Full Text:
    as P (xt|yt) = ∏ j P (xt,j|yt) . This assumption of independence of each input feature xt,j