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Filtering by: Creator Letchworth, Christopher A. Remove constraint Creator: Letchworth, Christopher A. Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan Decade 2000-2009 Remove constraint Decade: 2000-2009

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  • Protein secondary structure prediction using conditional random fields and profiles

    Creator:
    Shen, Rongkun
    Abstract:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
    Resource Type:
    Masters Thesis
    Full Text:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three

  • Image features and learning algorithms for biological, generic and social object recognition

    Creator:
    Zhang, Wei
    Abstract:
    Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require...
    Resource Type:
    Dissertation
    Full Text:
    : ______________________________________________________ Thomas G. Dietterich Automated recognition of object categories in images is a critical

  • Effective decision-theoretic assistance through relational hierarchical models

    Creator:
    Natarajan, Sriraam
    Abstract:
    Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...
    Resource Type:
    Dissertation
    Full Text:
    approved: Prasad Tadepalli Building intelligent computer assistants has been a long-cherished goal of AI

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
    Resource Type:
    Dissertation
    Full Text:
    direction of efficient training, we study the training of conditional random fields (CRFs), which provide a