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Filtering by: Contributor Bunch, Thomas R. Remove constraint Contributor: Bunch, Thomas R. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science

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  • Calibrating recurrent sliding window classifiers for sequential supervised learning

    Creator:
    Joshi, Saket Subhash
    Abstract:
    Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (RSW) classifier, which maps some window of the input sequence plus some number...
    Resource Type:
    Masters Thesis
    Full Text:
    Recurrent Sliding Window Classifiers for Sequential Supervised Learning Abstract approved: Thomas

  • 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:
    Recognition in Desktop Environments Abstract approved: Thomas G. Dietterich Knowledge workers are

  • Fabrication and characterization of p-type CuO / n-type ZnO heterostructure gas sensors prepared by sol-gel processing techniques

    Creator:
    Ravichandran, Ram
    Abstract:
    Increased interest in the field of sensor technology stems from the availability of an inexpensive and robust sensor to detect and quantify the presence of a specific gas. Bulk p-CuO/n-ZnO heterocontact based gas sensors have been shown to exhibit the necessary sensitivity and selectivity characteristics, however, low interfacial CuO/ZnO contact...
    Resource Type:
    Masters Thesis
    Full Text:
    Abstract approved: Thomas K. Plant Increased interest in the �eld of sensor technology stems from the

  • Design and analysis techniques for nano-joule ADCs and sampling linearity

    Creator:
    Brown, Thomas (Thomas William)
    Abstract:
    Two aspects of ADC system performance are addressed in this work. First, the combination of the ADC and its associated reference are co-designed for an energy constrained remote sensing system. Second, sampling linearity is mathematically analyzed as a function of frequency to provide enhanced understanding into an ADC's requisite sampling...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Thomas Brown for the

  • Model-based approximation methods for reinforcement learning

    Creator:
    Wang, Xin
    Abstract:
    The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
    Resource Type:
    Dissertation
    Full Text:
    Methods for Reinforcement Learning. Abstract approved: Thomas G Dietterich The thesis focuses on model

  • Simulator-Defined MDP Planning with Applications in Natural Resource Management

    Creator:
    Alkaee Taleghan, Majid
    Abstract:
    This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
    Resource Type:
    Dissertation
    Full Text:
    : Simulator-Defined MDP Planning with Applications in Natural Resource Management Abstract approved: Thomas

  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    , 2008. Title: Learning MDP Action Models via Discrete Mixture Trees Abstract approved: Thomas G

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
    Full Text:
    : Improving Automated Email Tagging with Implicit Feedback Abstract approved: Thomas G. Dietterich Machine

  • Three methods for estimating subpixel cover fractions in coarse resolution imagery

    Creator:
    Damon, Diane L.
    Abstract:
    Coarse resolution imagery, such as that produced by the MODIS instrument, poses the challenge of estimating sub-pixel proportions of di erent land cover types. This problem is di cult because of the variety and variability of vegetation within individual pixels. This thesis describes and compares two existing algorithms for estimating...
    Resource Type:
    Masters Thesis
    Full Text:
    Estimating Subpixel Cover Fractions in Coarse Resolution Imagery. Abstract approved: Thomas G. Dietterich

  • 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:
    : Thomas G. Dietterich Advances in sensor technology are greatly expanding the range of quantities that