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Filtering by: Creator Plantinga, Andrew J. Remove constraint Creator: Plantinga, Andrew J. Subject Machine learning Remove constraint Subject: Machine learning

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  • 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:
    . 21 Bibliography 1. Quinlan, J. R. (1999). “Some Elements of Machine Learning.” In

  • 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

  • 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

  • Exploiting monotonicity via logistic regression in Bayesian network learning

    Creator:
    Restificar, Angelo C., Dietterich, Thomas Glen, and Oregon State University. Department of Computer Science
    Abstract:
    An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting background knowledge. In this report, we present a theoretical analysis on the use...
    Resource Type:
    Technical Report
    Full Text:
    〉, define P (Y =j|c)= exp{β0j + β1jX1 + · · ·+ βnjXn} ∑kY−1 j′=0 exp{β0j′ + β1j′X1 + · · ·+ βnj′Xn} (3

  • 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:
    the within-cluster scatter is defined as min µk,cj J = M∑ j=1 K∑ k=1 cjk‖xj − µk‖22. The

  • 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:
    : Spatiotemporal Scaling of Species Diversity.” Ecology Letters 6 (8): 749–56. doi:10.1046/j.1461- 0248.2003.00497

  • 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:
    function between the two layers for a video frame is defined as: ERBM(z,h) = − ∑ j ∑ i L∑ l=1 Wijlhjzil

  • 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:
    feature vector and a feature along with all of the images respectively. For each real value feature Mi,j

  • 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:
    ][n]) = SemiSupervisedEM (S, Labels);3 where PointOwnership[i][j] is an estimate of the degree of

  • Parsing with Recurrent Neural Networks

    Creator:
    Cross, James Henry, III
    Abstract:
    Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...
    Resource Type:
    Dissertation
    Full Text:
    -standard dependency parsing system [42]. Stack S is a list of heads, j is the start index of the queue, and