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Filtering by: Creator Bartholomay, G. Andrew Remove constraint Creator: Bartholomay, G. Andrew Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Degree Level Master's Remove constraint Degree Level: Master's

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  • A Benchmarking Study of Unsupervised Anomaly Detection Algorithms

    Creator:
    Emmott, Andrew F.
    Abstract:
    It is common practice in the unsupervised anomaly detection literature to create experimental benchmarks by sampling from existing supervised learning datasets. We seek to improve this practice by identifying four dimensions important to real-world anomaly detection applications --- point difficulty, clusteredness of anomalies, relevance of features, and relative frequency of...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Andrew Emmott for the

  • Test fixture characterization for high-frequency silicon substrate parasitic extraction

    Creator:
    Tabalujan, Andrew R.
    Abstract:
    At frequencies exceeding 1-2 GHz, the reactive nature of a silicon substrate must be accounted in the substrate network models used in substrate coupling simulation. High-frequency substrate models, containing reactive components, must be validated through high-frequency network analyzer measurements. Prior fabricated test fixtures have been modified to enable high-frequency (up...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Andrew R. Tabalujan for the

  • Mental Models of Mere Mortals with Explanations of Reinforcement Learning

    Creator:
    Anderson, Andrew A.
    Abstract:
    How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124 participant, four-treatment experiment to compare participants’ mental models of an RL agent in the context of a simple Real-Time Strategy (RTS) game. The four treatments isolated...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Andrew A. Anderson for the

  • A probabilistic model for anomaly detection in remote sensor streams

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Remote sensors are becoming the standard for observing and recording ecological data in the field. Such sensors can record data at fine temporal resolutions, and they can operate under extreme conditions prohibitive to human access. Unfortunately, sensor data streams exhibit many kinds of errors ranging from corrupt communications to partial...
    Resource Type:
    Masters Thesis
    Full Text:
    Remote Sensor Streams Abstract approved: Thomas G. Dietterich Remote sensors are becoming the standard

  • A study of methods for fine-grained object classification of arthropod specimens

    Creator:
    Lin, Junyuan
    Abstract:
    Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
    Resource Type:
    Masters Thesis
    Full Text:
    Fine-grained Object Classification of Arthropod Specimens Abstract approved: Thomas G. Dietterich

  • Sequential supervised learning and conditional random fields

    Creator:
    Ashenfelter, Adam J.
    Abstract:
    Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems...
    Resource Type:
    Masters Thesis
    Full Text:
    Supervised Learning and Conditional Random Fields. Abstract approved: Thomas G. Dietterich Supervised

  • Learning greedy policies for the easy-first framework

    Creator:
    Xie, Jun
    Abstract:
    Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
    Resource Type:
    Masters Thesis
    Full Text:
    my thesis committee: Prof. Thomas G. Dietterich and Prof. Prasad Tadepalli, for their encouragement

  • Multi-criteria average reward reinforcement learning

    Creator:
    Natarajan, Sriraam
    Abstract:
    Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving scalar rewards, which the agent aims to maximize. But in many real world situations, tradeoffs must be made among multiple objectives. This necessitates the use...
    Resource Type:
    Masters Thesis
    Full Text:
    would like to thank Dr.Saurabh Sethia, Dr.Thomas G. Dietterich and Dr. John Bolte for sparing their

  • Species Distribution Modeling of Citizen Science Data as A Classification Problem with Class-Conditional Label Noise

    Creator:
    He, Liqiang
    Abstract:
    Species distribution models (SDM), which quantify the correlation between the distribution of a species and environmental factors, are increasingly used to map and monitor animal and plant distributions in the context of awareness of environmental change and its ecological consequence. For perfect data, this is a straightforward classification problem from...
    Resource Type:
    Masters Thesis
    Full Text:
    thank my thesis committee: Prof. Thomas G. Dietterish, Prof. Amir Nayyeri and Prof. John P. Bolte, for

  • On the Structure of Unconditional UC Hybrid Protocols

    Creator:
    Shirley, Morgan
    Abstract:
    Two parties, Alice and Bob, hold inputs x and y respectively. They wish to compute a function f of their inputs. In an ideal world, f(x, y) could be calculated by sending the inputs to a trusted third party. In the absence of such a third party, Alice and Bob...
    Resource Type:
    Masters Thesis
    Full Text:
    f . We introduce a trusted third party that can compute for Alice and Bob a different function g