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Filtering by: Creator Moerman, Kevin M. Remove constraint Creator: Moerman, Kevin M. Subject Machine learning Remove constraint Subject: Machine learning

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  • Detecting Fish Vocalizations in American Samoa Using Machine Learning

    Creator:
    Munger, Jill
    Abstract:
    A deep neural network was developed and trained to identify the sounds of pomacentrids (damselfishes) in the National Park of American Samoa. Four years of continuous, passive acoustic data were recorded by a single stationary hydrophone. Deep neural networks enable the full utilization of such large datasets by automating laborious...
    Resource Type:
    Presentation
    Full Text:
    credit: Kevin Bryant / CC BY ... ... 18,999 more hours of data to go. Machine Learning Training

  • Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale

    Creator:
    Hooper, Samuel
    Abstract:
    Developing accurate predictive distribution models requires adequately representing relevant spatial and temporal scales, as these scales are ultimately reflective of the relationships between distributions and influential environmental conditions. In this research, we considered both spatial and temporal scale and the influence each has on predicting broad-scale distributions of two disparate...
    Resource Type:
    Masters Thesis
    Full Text:
    impervious surface cover (ISC), for all of California, Oregon, and Washington at a 30 m spatial resolution

  • Learning greedy policies for the easy-first framework

    Creator:
    Xie, Jun
    Abstract:
    Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
    Resource Type:
    Masters Thesis

  • 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
    Full Text:
    the action class; and h denotes the space-time locations of the win- dows in A. For a set of M action

  • A study of methods for fine-grained object classification of arthropod specimens

    Creator:
    Lin, Junyuan
    Abstract:
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
    Resource Type:
    Masters Thesis
    Full Text:
    descriptors per image M Dimension of a local descriptor d Number of spatial bins for pyramid representation

  • 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:
    600 800 1000 1200 1400 1600 C u m u la ti v e M is ta ke s trained on user corrections

  • 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
    Full Text:
    , N.B. Venkateswarlu, KVSVN Raju and M. Shashi. I would especially like to thank both MNS and NBV for

  • Object detection in biological images using a search-based framework

    Creator:
    Lam, Michael
    Abstract:
    This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images for evolutionary biology research. In particular, our goal is to detect nematocysts in Scanning Electron Microscope (SEM) images. This biological domain presents challenges for existing...
    Resource Type:
    Masters Thesis

  • Applying machine learning for prediction, recommendation, and integration

    Creator:
    Bao, Xinlong
    Abstract:
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
    Resource Type:
    Dissertation
    Full Text:
    limited to Jed Irvine, Simone Stumpf, Michael Slater, Kevin Johnsrude, Jianqiang Shen, Lida Li, Twinkle

  • 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