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Browsing by Subject "machine learning"

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Browsing by Subject "machine learning"

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  • Activity recognition in desktop environments 
    Shen, Jianqiang (2009-03-19)
    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 ...
  • Applying machine learning for prediction, recommendation, and integration 
    Bao, Xinlong (2009-08-24)
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following t ...
  • A Bayesian approach to habitat suitability prediction 
    Lockett, Daniel Edwin IV (2012-03-27)
    For the west coast of North America, from northern California to southern Washington, a habitat suitability prediction framework was developed to support wave energy device siting. Concern that wave energy devices may ...
  • Efficient classification for longitudinal data 
    Wang, Xianlong (2010-01-21)
    We propose a new classification method for longitudinal data based on a semiparametric approach. Our approach builds a classifier by taking advantage of modeling information between response and covariates for each class ...
  • End-user debugging of machine-learned programs : toward principles for baring the logic 
    Kulesza, Todd; Stumpf, Simone; Riche, Yann; Burnett, Margaret; Wong, Weng-Keen; Oberst, Ian; Moor, Travis; McIntosh, Kevin; Bice, Forrest (2009?)
    Many applications include machine learning algorithms intended to learn “programs” (rules of behavior) from an end user’s actions. When these learned programs are wrong, their users receive little explanation as to why, ...
  • End-user feature engineering in the presence of class imbalance 
    Oberst, Ian; Moore, Travis; Wong, Weng-Keen; Kulesza, Todd; Stumpf, Simone; Riche, Yann; Burnett, Margaret (2009-11-02)
    Intelligent user interfaces, such as recommender systems and email classifiers, use machine learning algorithms to customize their behavior to the preferences of an end user. Although these learning systems are somewhat ...
  • Evaluating online text classification algorithms for email prediction in TaskTracer 
    Keiser, Victoria L (2009-06-17)
    This paper examines how six online multiclass text classification algorithms perform in the domain of email tagging within the TaskTracer system. TaskTracer is a project-oriented user interface for the desktop knowledge ...
  • Image features and learning algorithms for biological, generic and social object recognition 
    Zhang, Wei (2009-03-16)
    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 variabili ...
  • Learning ranking functions for efficient search 
    Xu, Yuehua (2010-08-12)
    This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, a ...
  • On feature relevance feedback methods : incorporating labeled user features 
    Oberst, Ian (2010-08-06)
    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 d ...
  • Predictive mapping of landtype association maps in three Oregon national forests 
    Peterman, Wendy L. (2010-07-01)
    This study explores the use of predictive mapping techniques in developing Landtype Association (LTA) maps for use in natural resource management. These maps are produced for the USDA Forest Service on a regional basis ...
  • Rare category detection using hierarchical mean shift 
    Vatturi, Pavan Kumar (2009-01-22)
    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 ...
  • Toward end-user debugging of machine-learned programs 
    Kulesza, Todd (2009-12-02)
    The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters because without the ability to fix e ...

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