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Filtering by: Creator Duringer, J. M. Remove constraint Creator: Duringer, J. M. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Subject Algorithms Remove constraint Subject: Algorithms

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  • Shrunken learning rates do not improve AdaBoost on benchmark datasets

    Creator:
    Forrest, Daniel L. K.
    Abstract:
    Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman...
    Resource Type:
    Masters Thesis
    Full Text:
    , J., Bartlett, P., and Frean, M. (2000). Boosting algorithms as gradient descent. In Advances in

  • A factoring approach for probabilistic inference in belief networks

    Creator:
    Li, Zhaoyu
    Abstract:
    Reasoning about any realistic domain always involves a degree of uncertainty. Probabilistic inference in belief networks is one effective way of reasoning under uncertainty. Efficiency is critical in applying this technique, and many researchers have been working on this topic. This thesis is the report of our research in this...
    Resource Type:
    Dissertation
    Full Text:
    typical node x has n parents u1,..., un and m children y1, ...y, the belief distri- bution of variable x

  • Ensemble Monte-Carlo planning : an empirical study

    Creator:
    Lewis, Paul
    Abstract:
    Monte-Carlo planning algorithms such as UCT make decisions at each step by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and make a...
    Resource Type:
    Masters Thesis
    Full Text:
    arrays of rewards and visits to each action node} 3: (r, v)← SparseUCT (s0, t, c, ss) 4: for j = 1 to