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Filtering by: DOI https://doi.org/10.1890/12-1179.1 Remove constraint DOI: https://doi.org/10.1890/12-1179.1 Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • 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:
    -linearity is applied to the linear combination, giving the 12 following formula for the parent

  • Listing as a Potential Connection between Sets of Outcomes and Counting Processes

    Creator:
    Erickson, Sarah A.
    Abstract:
    Counting problems are rich in opportunities for students to make meaningful mathematical connections and develop non-algorithmic thinking; their accessible nature and applications to computer science make counting problems a valuable part of mathematics curricula. However, students struggle in various ways with counting, and while previous studies have indicated that listing...
    Resource Type:
    Masters Thesis
    Full Text:
    Perspectives: Lockwood’s (2013) Model of Students’ Combinatorial Thinking ………………………………………………...12 3 Methods

  • Scaling multiagent reinforcement learning

    Creator:
    Proper, Scott
    Abstract:
    Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . 12 2.4 Model-free Reinforcement Learning . . . . . . . . . . . . . . . . . . 13 2.5 Model-based

  • Machine learning for improving the quality of citizen science data

    Creator:
    Yu, Jun
    Abstract:
    Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute...
    Resource Type:
    Dissertation
    Full Text:
    Occupancy-Detection-Expertise model. . . . . . . . . . . . 12 3.3 Occupancy, Detection and Expertise

  • Protocol design and optimization for wireless networks

    Creator:
    Lertsutthiwong, Monchai
    Abstract:
    We investigate a number of techniques for increasing throughput and quality of media applications over wireless networks. A typical media communication application such as video streaming imposes strict requirements on the delay and throughout of its packets, which unfortunately, cannot be guaranteed by the underlying wireless network due inherently to...
    Resource Type:
    Dissertation
    Full Text:
    Streaming for Single-HopWireless Networks using a Contention- Based Access MAC Protocol 12 2.1

  • Monte Carlo Counterfactual Regret Minimization applied to Clue

    Creator:
    Martin, Benjamin
    Abstract:
    This document analyzes the application of Monte Carlo Counterfactual Regret Minimization (MCCFR) in the game of Hasboro’s Clue. As a partially observable stochastic multiplayer game, Clue is well-suited for MCCFR methods. MCCFR has previously been shown to be effective in beating top human players around the world in No-Limit Texas...
    Resource Type:
    Honors College Thesis
    Full Text:
    Scholar) Presented June 12, 2020 Commencement June 2021 AN ABSTRACT OF THE THESIS OF Benjamin

  • 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:
    . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.4 Clustering Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.5

  • Learning multiple non-redundant codebooks with word clustering for document classification

    Creator:
    Surve, Akshat Sudhakar
    Abstract:
    The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
    Resource Type:
    Masters Thesis
    Full Text:
    presented on June 12, 2009 Title: Learning Multiple Non-redundant Codebooks with Word Clustering for

  • Toward Automated Decision-Making in Power Systems Wide-Area Protection

    Creator:
    Meier, Richard John
    Abstract:
    In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements and despite targeted efforts to improve the operation and control of the electric grid, the power systems industry has seen relatively small advances in this regard. For instance, today's power system is...
    Resource Type:
    Masters Thesis
    Full Text:
    Science in Electrical and Computer Engineering presented on June 12, 2015. Title: Toward Automated

  • A Deep Action Segmentation and Its Explanation with a Dictionary of Meaningful Attention Maps

    Creator:
    Trigkakis, Dimitrios
    Abstract:
    This thesis addresses the problem of temporal action segmentation in videos, where the goal is to label every video frame with the appropriate action class present. We focus on the domain of NFL football videos, where action classes represent common football play types. For action segmentation, we use a temporal...
    Resource Type:
    Masters Thesis
    Full Text:
    Datasets 12 5 Experiments of Video Analysis 14 5.1 Viewpoint experiments