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Filtering by: Creator Thomas, Christoph K. Remove constraint Creator: Thomas, Christoph K. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Interactive football playbook

    Creator:
    Neumann, Christoph
    Abstract:
    This thesis presents a domain specific visual language designed to allow coaches to create content that exhibits the complex 2D interactions observed in the game of American football. Coaches can visually program the content by using symbols and drawing primitives similar to those that they currently use to design static...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Christoph Neumann for the degree of

  • An investigation of the search space of Lenat's AM program

    Creator:
    Mishra, Prafulla K.
    Abstract:
    AM is a computer program written by Doug Lenat that discovers elementary mathematics starting from some initial knowledge of set theory. The success of this program is not clearly understood. This work is an attempt to explore the search space of AM in order to understand the success and eventual...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Prafulla K. Mishra for the degree of Master of Science in Computer

  • 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:
    AN ABSTRACT OF THE THESIS OF Daniel L. K. Forrest for the

  • Machine learning methods for public policy : simulation, optimization, and visualization

    Creator:
    McGregor, Sean
    Abstract:
    Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for...
    Resource Type:
    Dissertation
    Full Text:
    Methods for Public Policy: Simulation, Optimization, and Visualization Abstract approved: Thomas G

  • Partitioning-Guided K-Means for Effective Empty Cluster Resolution in Extreme Model Compression

    Creator:
    Huang, Tianhong
    Abstract:
    Compactness in deep learning can be critical to a model’s viability in low-resource applications, and a common approach to extreme model compression is quantization. We consider Iterative Product Quantization (iPQ) with Quant-Noise [Fan et al., 2020] to be state-of-the-art in this area, but this quantization framework suffers from preventable inference...
    Resource Type:
    Masters Thesis
    Full Text:
    degree of Master of Science in Computer Science presented on May 31, 2023. Title: Partitioning-Guided K

  • Learning hierarchical decomposition rules for planning : an inductive logic programming approach

    Creator:
    Reddy, Chandrasekhara K.
    Abstract:
    Artificial Intelligence (AI) planning techniques have been central to automating a gamut of tasks from the mundane route planning and beer production to the ethereal image processing of space-ship images. Of all the planning techniques, hierarchical- decomposition planning has been the technique most employed in industrial-strength planners. Hierarchical-decomposition planning is...
    Resource Type:
    Dissertation
    Full Text:
    doc.dvi AN ABSTRACT OF THE THESIS OF Chandrasekhara K. Reddy for the

  • Leaf shape recognition via support vector machines with edit distance kernels

    Creator:
    Sinha, Shriprakash
    Abstract:
    Edit distances are a well-established technique for classification problems. They have been employed successfully in many classification problems including chromosome classification and hand-written digit recognition. Virtually all machine learning algorithms represent the objects to be classified as vectors of features. However, edit distances provide only a measure of the difference...
    Resource Type:
    Masters Thesis

  • A study of distance-based machine learning algorithms

    Creator:
    Wettschereck, Dietrich
    Abstract:
    Distance-based algorithms are machine learning algorithms that classify queries by computing distances between these queries and a number of internally stored exemplars. Exemplars that are closest to the query have the largest in uence on the classi cation assigned to the query. Two speci c distance-based algorithms, the nearest neighbor...
    Resource Type:
    Dissertation
    Full Text:
    Distance-Based Machine Learning Algorithms Abstract approved: Thomas G. Dietterich Distance-based

  • 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

  • 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:
    dissertation with important feedback; Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, and Casey Dugan, for