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Filtering by: Creator Riscoe, Erin W. Remove constraint Creator: Riscoe, Erin W. Non-Academic Affiliation Oregon State University. Graduate School Remove constraint Non-Academic Affiliation: Oregon State University. Graduate School Subject Machine learning Remove constraint Subject: Machine learning

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  • 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:
    parameter vector w . . . . . . . . . . . 52 4.3 Relative inference error with different map size

  • Activity recognition in desktop environments

    Creator:
    Shen, Jianqiang
    Abstract:
    Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
    Resource Type:
    Dissertation
    Full Text:
    limited to Xinlong Bao, Chen Cao, Fredric Carl, Phuoc Do, Erin Fitzhenry, Michael Goodman, Brenton

  • 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:
    Lettkeman, Erin Fitzhenry, Wei Wu, Jill Cao, Ben Porter and Phuoc Do. It has been a great pleasure working