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  • Theoretical and implementation improvements for difference evaluation functions

    Creator:
    Colby, Mitchell Kataichi
    Abstract:
    Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each...
    Resource Type:
    Dissertation

  • Machine learning for computer architecture design and optimization

    Creator:
    Penney, Drew
    Abstract:
    Machine learning applied to computer architecture has rapidly transitioned from a theoretical novelty to being a driving force behind design, control, and simulation in practically all components. These machine-learning-based methodologies are further notable for their scalability to increasingly complex design challenges, which has allowed these methodologies to surpass the prior...
    Resource Type:
    Dissertation
    Full Text:
    degree of Doctor of Philosophy in Electrical and Computer Engineering presented on April 11, 2023

  • Machine learning for activity recognition

    Creator:
    Oregon State University. Dept. of Computer Science and Shen, Jianqiang
    Abstract:
    This paper surveys the activity recognition task from a machine learning perspective. I give a definition of this problem, and I classify different activity recognition problems into two categories. I show the activities can be hierarchical, and based on such hierarchies I synthesize a language to describe activities. I give...
    Resource Type:
    Research Paper
    Full Text:
    . At the same time, as the computer and Internet become more and more popular, computer crimes have

  • Interacting meaningfully with machine learning systems : three experiments

    Creator:
    Oregon State University. Dept. of Computer Science, Stumpf, Simone, Rajaram, Vidya, Li, Lida, Wong, Weng-Keen, Burnett, Margaret, 1949-, Dietterich, Thomas Glen, Sullivan, Erin, and Herlocker, Jonathan Lee
    Abstract:
    Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple "right/wrong" judgments. If the users themselves could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved...
    Resource Type:
    Research Paper
    Full Text:
    Oregon State University School of Electrical Engineering and Computer Science Corvallis, OR 97331 USA

  • 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:
    software tool that helps the user quickly locate files on the computer. It predicts the file folder that

  • 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:
    of Doctor of Philosophy in Computer Science presented on December 7, 2018. Title: Pixel- and Frame

  • 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:
    the degree of Doctor of Philosophy in Computer Science presented on December 8, 2016. Title: Parsing

  • Predicting activity type from accelerometer data

    Creator:
    Zheng, Yonglei
    Abstract:
    The study of physical activity is important in improving people’s health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has...
    Resource Type:
    Masters Thesis
    Full Text:
    degree of Master of Science in Computer Science presented on August 17, 2012. Title: Predicting

  • 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
    Full Text:
    Computer Science presented on December 1, 2014. Title: Personalizing Machine Learning Systems with

  • Toward end-user debugging of machine-learned programs

    Creator:
    Kulesza, Todd
    Abstract:
    The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters because without the ability to fix errors, users may find that the learned program’s errors...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Todd Kulesza for the degree of Master of Science in Computer Science