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  • 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:
    learning and data mining. In document classification, most of the popular algorithms are based on the

  • 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:
    with length l do 7: x← Featurize(s) 8: training data← training data ∪ (x, label) 9: end for 10: end

  • Efficient Algorithms for Robust Spatiotemporal Data Analysis

    Creator:
    Moore, Travis W.
    Abstract:
    Many large-scale data analysis applications involve data that can vary over both time and space. Often the primary goal of analyzing spatiotemporal data is identifying trends, movements, and sudden changes with respect to time, location, or both. This can include a variety of applications in economics (housing prices, unemployment, job...
    Resource Type:
    Dissertation
    Full Text:
    Algorithms for Robust Spatiotemporal Data Analysis Abstract approved: Weng-Keen Wong Many large-scale data

  • 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:
    improved accuracy and a 180-fold speedup on real user data. To recognize workflows, we first mine

  • 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:
    States Department of the Interior used eBird data as the basis for the 2011 State of the Birds Report

  • Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

    Creator:
    Zemicheal, Tadesse H.
    Abstract:
    Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
    Resource Type:
    Dissertation
    Full Text:
    : Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control Abstract approved

  • Weakly Supervised Learning for Activity Recognition from Time Series Data

    Creator:
    Guan, Xinze
    Abstract:
    The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
    Resource Type:
    Dissertation
    Full Text:
    Learning for Activity Recognition from Time Series Data Abstract approved: Weng-Keen Wong Raviv Raich

  • Behavior graphs for data-driven animation of 3d characters

    Creator:
    Srinivasan, Madhusudhanan
    Abstract:
    In this dissertation, we present a user-in-the-loop method for the design of an interactive motion data structure that benefits from the advantages of both motion graphs and blend-based techniques. Our novel approach automatically analyzes a traditional motion graph built from labeled motion clips. The result is a more condensed, coarser...
    Resource Type:
    Dissertation
    Full Text:
    - sive and natural character motion. . . . . . . . . . . . . . . . . . . 7 2.1 A classification of data

  • Fine-Grained Object Recognition Under Limited Training Data

    Creator:
    Lam, Michael Q.
    Abstract:
    This dissertation addresses object recognition in challenging settings, where distinct object classes are visually very similar (e.g., species of birds and insects) and/or access to training examples of object classes is limited (e.g., due to the associated high costs of data annotation). In this dissertation, we present a variety of...
    Resource Type:
    Dissertation
    Full Text:
    Object Recognition Under Limited Training Data Abstract approved: Sinisa Todorovic This dissertation

  • Probabilistic models for quality control in environmental sensor networks

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
    Resource Type:
    Dissertation
    Full Text:
    manifests as corrupt or missing measurements in the data. Consequently, before the data can be used in