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Filtering by: Creator Mishra, Prafulla K. Remove constraint Creator: Mishra, Prafulla K. Subject Machine learning Remove constraint Subject: Machine learning

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  • 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:
    principal components analysis (PCA) and k-means clustering. PCA provides a method to extract significant

  • Improving robotic grasping performance using machine learning techniques

    Creator:
    Goins, Alex Keith
    Abstract:
    Robots are being utilized in ever more complex tasks and environments to help humans with difficult or dangerous tasks. However, robotic grasping is still in its infancy and is one of the limiting factors which prevent the deployment of robots in the home and other assisted living scenarios. Traditional methods...
    Resource Type:
    Masters Thesis
    Full Text:
    distribution over functions f(x). In our work, each data instance xi is a grasp, which has k features that

  • Anomaly Detection: Theory, Explanation and User Feedback

    Creator:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
    Resource Type:
    Dissertation
    Full Text:
    for the Three Scoring Methods (IF, AVE, MIN) with Varying Pattern Space Complexity k Over Three