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Filtering by: Creator Treuden, Michael L. Remove constraint Creator: Treuden, Michael L. 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:
    Microsoft Word - MS Thesis 6.doc AN ABSTRACT OF THE THESIS OF Michael

  • 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
    Full Text:
    Framework AN ABSTRACT OF THE THESIS OF Michael Lam for the degree of Master of Science in Computer

  • 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
    Full Text:
    different numbers of cells L . . . . . 54 5.1 TTK improves the SVM decision boundary (“d. b.”). Square

  • Towards a national map of soil liquefaction susceptibility : modeling with heuristic and geotechnical methods

    Creator:
    Lindsley, Adam E.
    Abstract:
    This study investigates the use of predictive mapping techniques as well as geotechnical criteria in developing a multiregional soil liquefaction model and subsequent maps. The maps were produced using National Cooperative Soil Survey data, in the gSSURGO format, combined with soil liquefaction data gathered from studies, articles, and traditional seismic...
    Resource Type:
    Masters Thesis
    Full Text:
    challenging project. Thank you also to my committee members, Maria Dragila, John Baham, and Michael Olsen

  • 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
    Full Text:
    Tadepalli provided useful feedback on EBL related issues and proof-read most of my papers. Michael Scanlan

  • 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
    Full Text:
    retired. To each of my committee members—Dr. John Fowler, Dr. Jeffrey Anderson, Dr. Michael Freitag

  • 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

  • Learning cost-sensitive diagnostic policies from data

    Creator:
    Zubek, Valentina Bayer
    Abstract:
    In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body tem- perature, weight, etc.) and laboratory measurements (white blood count, CT scan,...
    Resource Type:
    Dissertation
    Full Text:
    Computer Science department, my professors over the years who taught me so much: Paul Cull, Michael Quinn

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

  • 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:
    . References Addicott, John F., John M. Aho, Michael F. Antolin, Dianna K. Padilla, John S. Richardson, and