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Filtering by: Creator Mosier, Thomas M. Remove constraint Creator: Mosier, Thomas M. Advisor Dietterich, Thomas G. Remove constraint Advisor: Dietterich, Thomas G.

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  • Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

    Creator:
    Dodge, Jonathan E.
    Abstract:
    Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . 180 M Data Appendix - CNNs poisoned by perception/action . . . . . . . . . . . . 182 TABLE OF

  • Column Time Warping with Neighborhood Distortion Cost

    Creator:
    Kucey, Brennan Mark
    Abstract:
    Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
    Resource Type:
    Honors College Thesis
    Full Text:
    : ______________________________________________________ Thomas Dietterich The Cornell Laboratory of Ornithology coordinates the eBird

  • Model-based approximation methods for reinforcement learning

    Creator:
    Wang, Xin
    Abstract:
    The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
    Resource Type:
    Dissertation
    Full Text:
    Methods for Reinforcement Learning. Abstract approved: Thomas G Dietterich The thesis focuses on model

  • Understanding and improving error-correcting output coding

    Creator:
    Kong, Eun Bae
    Abstract:
    Error-correcting output coding (ECOC) is a method for converting a k-classsupervised learning problem into a large number L of two-class supervised learningproblems and then combining the results of these L evaluations. Previous researchhas shown that ECOC can dramatically improve the classi cation accuracy of supervisedlearning algorithms that learn to classify...
    Resource Type:
    Dissertation
    Full Text:
    and Improving Error-Correcting Output Coding Abstract approved: Thomas G. Dietterich Error

  • Concept coverage and its application to two learning tasks

    Creator:
    Almuallim, Hussein Saleh
    Abstract:
    The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size for given accuracy and confidence parameters. This thesis begins by asking whether good learning algorithms can be designed by maximizing their coverage. There are three questions...
    Resource Type:
    Dissertation
    Full Text:
    Tasks Abstract approved Thomas G. Dietterich The coverage of a learning algorithm is the number of

  • Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

    Creator:
    Zemicheal, Tadesse H.
    Abstract:
    Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
    Resource Type:
    Dissertation
    Full Text:
    : Thomas G. Dietterich Advances in sensor technology are greatly expanding the range of quantities that

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
    Full Text:
    : Improving Automated Email Tagging with Implicit Feedback Abstract approved: Thomas G. Dietterich Machine

  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    , 2008. Title: Learning MDP Action Models via Discrete Mixture Trees Abstract approved: Thomas G

  • 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:
    Learning Rates Do Not Improve AdaBoost On Benchmark Datasets. Abstract approved: Thomas G. Dietterich

  • 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:
    Feature Induction in Sequential Supervised Learning Abstract approved: Thomas G. Dietterich Sequential