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Filtering by: Creator Blaustein, Andrew R. Remove constraint Creator: Blaustein, Andrew R. Subject Machine learning Remove constraint Subject: Machine learning

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  • Special Nuclear Material Analysis Using Temporal Gamma-Ray Spectroscopy and Machine Learning Methods

    Creator:
    Curtis, Jessica R.
    Abstract:
    Following thermal neutron induced fission, supervised machine learning and temporal gamma-ray spectroscopy methods were used to identify differences in the delayed gamma-ray spectra of Pu-239 and U-235. The temporal gamma-ray spectroscopy method takes advantage of the time-dependent decay of fission products. Employing Spearman's rank-order correlation coefficient and without prior knowledge...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Jessica R. Curtis for the

  • Learning classification rules by randomized iterative local search

    Creator:
    Chisholm, Michael (Michael Andrew), 1974-
    Abstract:
    Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning algorithms for this problem employ some variation of a greedy separate-and-conquer algorithm. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a...
    Resource Type:
    Masters Thesis
    Full Text:
    */ for r = 1 to M do pick random seed example of class c and create rule rule from it for t

  • 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:
    estimator [32]. All descriptor vectors that are within a fixed radius r of the cluster center are assigned

  • 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:
    , Ginger Ngo, Andrew Pask, Taal Levy, Tom Munger, friends and family! References Bergler, C., H

  • 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:
    (1): 149–72. doi:10.1146/annurev-ecolsys-102209-144636. Elith, Jane, and John R. Leathwick. 2009

  • Image Based Crypto

    Creator:
    Basa, Gungor
    Abstract:
    The ability to create reproducible cryptographically secure keys from temporal environments (e.g., images) has the potential to be a contributor to effective cryptographic mechanisms. Due to the noisy nature of these environments, achieving this goal in a user friendly fashion is a very challenging task, especially since there exists a...
    Resource Type:
    Masters Thesis
    Full Text:
    secret respectively. q and p be primes and q, r are co prime to each other such that |q| = κ, |r| = `, p

  • Rare category detection using hierarchical mean shift

    Creator:
    Vatturi, Pavan Kumar
    Abstract:
    Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
    Resource Type:
    Masters Thesis
    Full Text:
    the distance between this example and its Kthi4 nearest neighbor; Set r ′ i to be the minimum value

  • 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
    Full Text:
    takes previous RTDF predictions yr

  • 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
    Full Text:
    function L(x, y′, y) : X ×Y ×Y 7→ R+ associates a loss with labeling an input xwith y′ when its true output

  • 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:
    , Drew Bagnell, Roni Khardon, Andrew McCallum, Lise Getoor, Jason Eisner, Hal Daumé III, John Langford