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Filtering by: Creator Michael, Todd P. Remove constraint Creator: Michael, Todd P. Degree Level Doctoral Remove constraint Degree Level: Doctoral Subject Machine learning Remove constraint Subject: Machine learning

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  • Personalizing machine learning systems with explanatory debugging

    Creator:
    Kulesza, Todd
    Abstract:
    How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...
    Resource Type:
    Dissertation
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    Debugging AN ABSTRACT OF THE DISSERTATION OF Todd Kulesza for the degree of Doctor of Philosophy in

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
    Resource Type:
    Dissertation
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    Modes for Sequential Decision Making Abstract approved: Alan P. Fern This thesis considers the

  • Investigating Transcriptional Control of Specialized Gene Expression in Plants

    Creator:
    Fraser, Valerie N.
    Abstract:
    Specialized or secondary metabolism is a collection of pathways and small molecules that, while beneficial to an organism, are not strictly necessary for survival. Plants use secondary metabolites to, among other things, attract pollinators, defend against biotic and abiotic stressors, and form symbioses. Natural products from plants have seen an...
    Resource Type:
    Dissertation
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    retired. To each of my committee members—Dr. John Fowler, Dr. Jeffrey Anderson, Dr. Michael Freitag

  • Machine learning in engineering : techniques to speed up numerical optimization

    Creator:
    Cerbone, G. (Giuseppe)
    Abstract:
    Many important application problems in engineering can be formalized as nonlinear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. For example, these methods depend critically on choosing a good starting point from which to perform the optimization search. In high-dimensional spaces, numerical...
    Resource Type:
    Dissertation
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    Tadepalli provided useful feedback on EBL related issues and proof-read most of my papers. Michael Scanlan

  • Robust and Efficient Classification of Videos in the Wild

    Creator:
    Mahasseni, Behrooz
    Abstract:
    Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
    Resource Type:
    Dissertation
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    , Anirban Roy, Michael Lam, Sheng Chen, Peng Lei, Christos Margiolas, Alberto Garcia, Suren Jayasuriya and

  • Integrating learning and search for structured prediction

    Creator:
    Doppa, Janardhan Rao
    Abstract:
    We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
    Resource Type:
    Dissertation
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    : Integrating Learning and Search for Structured Prediction Abstract approved: Prasad Tadepalli Alan P. Fern

  • Machine Learning Methods for Computational Sustainability

    Creator:
    Liu, Liping
    Abstract:
    Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
    Resource Type:
    Dissertation
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    observed label set Yn is drawn from a set-valued distribution p(Yn|yn) that depends only on the true label

  • Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

    Creator:
    Briggs, Forrest
    Abstract:
    We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their...
    Resource Type:
    Dissertation

  • 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
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    thanks to Michael Slater, who is not only the primary devel- oper of the software (TAPE) used in this

  • Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

    Creator:
    Lei, Peng
    Abstract:
    This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
    Resource Type:
    Dissertation
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    University: Mohamed Amer, Sheng Chen, Liping Liu, Jun Li, Anirban Roy, Behrooz Mahasseni, Michael Lam