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Filtering by: Creator Gabriel, John T. Remove constraint Creator: Gabriel, John T. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Creator Hao, Guohua Remove constraint Creator: Hao, Guohua Non-Academic Affiliation Oregon State University. Graduate School Remove constraint Non-Academic Affiliation: Oregon State University. Graduate School

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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:
    discussions on this thesis work, and Dr. John Langford for discussion of the counter example to independent

  • 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:
    each Yi = (yi,1, . . . , yi,Ti) is a correspond- ing sequence of class labels, where yi,t ∈ Y = {1