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Filtering by: Creator Merriam, Lawrence C. Remove constraint Creator: Merriam, Lawrence C. Creator Hao, Guohua Remove constraint Creator: Hao, Guohua Degree Field Computer Science Remove constraint Degree Field: Computer Science

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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:
    overhead. c©Copyright by Guohua Hao February 27, 2009 All Rights Reserved Revisiting Output Coding

  • 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:
    range dependencies in sequences. c©Copyright by Guohua Hao July 21, 2009 All Rights Reserved