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Filtering by: Creator Archila, Kevin A. Remove constraint Creator: Archila, Kevin A. Contributor advisor sim Dietterich, Thomas G. Remove constraint Contributor advisor sim: Dietterich, Thomas G. Degree Name Master of Science (M.S.) Remove constraint Degree Name: Master of Science (M.S.)

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  • 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:
    Computer Science presented on October 25th, 2007. Title: A Probabilistic Model for Anomaly Detection in

  • 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:
    Master of Science in Computer Science presented on February 18, 2013. Title: A Study of Methods for

  • The Familiarity Hypothesis: Explaining the Behavior of Deep Learning Methods in Out-of-Distribution Detection

    Creator:
    Guyer, Alexander
    Abstract:
    Many object recognition applications require detecting and responding to objects drawn from a different distribution from that of the training data. This task is referred to as out-of-distribution (OOD) detection, and it is often formulated as an outlier detection problem wherein the probability distribution of the known data P(X) is...
    Resource Type:
    Masters Thesis
    Full Text:
    responding to objects drawn from a different distribution from that of the training data. This task is

  • Learning functional descriptions from examples

    Creator:
    Flann, Nicholas S.
    Abstract:
    The task of inductive learning from examples places constraints on the representation of training instances and concepts. These constraints are different from, and often incompatible with, the constraints placed on the representation by the performance task. This incompatibility is severe when learning functional concepts and explains why previous researchers have...
    Resource Type:
    Masters Thesis
    Full Text:
    so difficult to construct good representations for inductive learningthey were trying to achieve a