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Filtering by: Committee Member Minoura, Toshimi Remove constraint Committee Member: Minoura, Toshimi Advisor Tadepalli, Prasad Remove constraint Advisor: Tadepalli, Prasad

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  • A study of model-based average reward reinforcement learning

    Creator:
    Ok, DoKyeong
    Abstract:
    Reinforcement Learning (RL) is the study of learning agents that improve their performance from rewards and punishments. Most reinforcement learning methods optimize the discounted total reward received by an agent, while, in many domains, the natural criterion is to optimize the average reward per time step. In this thesis, we...
    Resource Type:
    Dissertation
    Full Text:
    : Bella Bose, Toshimi Minoura, Vikram Saletore, and Robert. Robson for their encouragement.. I would

  • Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

    Creator:
    Liere, Ray
    Abstract:
    We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...
    Resource Type:
    Dissertation
    Full Text:
    committee – Prof. Prasad Tadepalli, Prof. Curtis Cook, Prof. Toshimi Minoura, Dr. William Hersh, Prof