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  • Dimensionality Reduction Techniques for Compositional Data : An Empirical Study

    Creator:
    George, Rogen
    Abstract:
    Compositional data is a type of data where the features are non-negative and always sum to a constant. This type of data is frequently encountered in many fields such as microbiology, geology, economics and natural language processing. Compositional data has unique statistical properties that makes it difficult to apply standard...
    Resource Type:
    Masters Thesis
    Full Text:
    Techniques for Compositional Data : An Empirical Study Abstract approved: Weng Keen Wong Compositional

  • Counterfactual States for Atari Agents via Generative Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
    Resource Type:
    Masters Thesis
    Full Text:
    States for Atari Agents via Generative Deep Learning Abstract approved: Weng-Keen Wong Although deep

  • Visualization and analysis of sensor data for detecting microclimate cold air pools

    Creator:
    Singh, Satpreet Harcharan
    Abstract:
    Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like this lead to microclimate anomalies such as the city of Corvallis (Oregon) experiencing persistent cold weather even on a sunny day. We analyze multivariate temperature...
    Resource Type:
    Masters Thesis
    Full Text:
    Singh, Author ACKNOWLEDGEMENTS Firstly, I would like to thank my advisor, Dr. Weng-Keen Wong, for

  • Incorporating labeled features into image classification using generalized expectation

    Creator:
    Moore, Travis
    Abstract:
    Image classification is a difficult problem, often requiring large training sets to get satisfactory results. However this is a task that humans perform very well, and incorporating user feedback into these learning algorithms could help reduce the dependency on large amounts of labeled training data. This process has already been...
    Resource Type:
    Masters Thesis
    Full Text:
    Features into Image Classification using Generalized Expectation Abstract approved: Weng-Keen Wong

  • Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models

    Creator:
    Anderson, Michael M.
    Abstract:
    Physical activity recognition using accelerometer data is a rapidly emerging field with many real-world applications. Much of the previous work in this area has assumed that the accelerometer data has already been segmented into pure activities, and the activity recognition task has been to classify these segments. In reality, activity...
    Resource Type:
    Masters Thesis
    Full Text:
    Markov Models Abstract approved: Weng-Keen Wong Physical activity recognition using accelerometer data

  • 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:
    Activity Type from Accelerometer Data Abstract approved: Weng-Keen Wong The study of physical activity

  • On feature relevance feedback methods : incorporating labeled user features

    Creator:
    Oberst, Ian
    Abstract:
    In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature relevance feedback has the potential to dramatically increase classification performance. We review previous work on incorporating feature relevance feedback in the form of labeled features...
    Resource Type:
    Masters Thesis
    Full Text:
    Labeled User Features Abstract approved: Weng-Keen Wong In text classification, labeling features is

  • 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:
    using Hierarchical Mean Shift Abstract approved: Weng-Keen Wong Many applications in surveillance

  • Classification of motion capture sequences

    Creator:
    Maniar, Dharin Haresh
    Abstract:
    Motion capture data is a digital representation of the complex temporal structure of human motion. Motion capture is widely used for data-driven animation in sports,medicine and entertainment, because of its ability to capture complex and realistic motions. Due to its efficiency and cost, methods for reusing collections of motion capture...
    Resource Type:
    Masters Thesis
    Full Text:
    . Title: Classification of Motion Capture Sequences Abstract approved: Weng-Keen Wong Motion

  • Prediction of gene transcription start sites and initiation patterns from DNA sequence content

    Creator:
    Morton, Taj H.
    Abstract:
    The computational identification of gene Transcription Start Sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are...
    Resource Type:
    Masters Thesis
    Full Text:
    . Abstract approved: ______________________________________________________ Molly Megraw, Weng-Keen Wong