Now showing items 1-5 of 5

    • Graph-based Learning Using A Naive Bayesian Classifier 

      Hawes, Robert (Computer Science & Engineering, 2007-08-23)
      Graph-based data representation is becoming increasingly more commonplace, as graphs can represent some kinds of data more efficiently than relational tables. As such, interesting patterns in the form of subgraphs can be ...
    • Investigation Of Techniques To Increase The Scalability Of Graph-based Data Mining Algorithms. 

      Inavolu, Srilatha (Computer Science & Engineering, 2007-08-23)
      Frequent subgraph pattern recognition and graph-based relational learning have been an emerging area of data mining research with scientific and commercial applications. At the kernel of these algorithms are the ...
    • Learning Video Preferences Using Visual Features And Closed Captions 

      Brezeale, Darin (Computer Science & Engineering, 2008-04-22)
      Viewers of video now have more choices than ever. As the number of choices increases, the task of searching through these choices to locate video of interest is becoming more difficult. Current methods for learning a ...
    • Monitoring Health By Detecting Drifts And Outliers In Patterns Of An Inhabitant In A Smart Home 

      Jain, Gaurav (Computer Science & Engineering, 2007-08-23)
      The elderly, along with people with disabilities or chronic illness, are most often dependent on some kind of formal or informal care. They are forced to move to a place where they can be cared for. Automatic health ...
    • Supervised Learning From Embedded Subgraphs 

      Potts, Joseph T. (Computer Science & Engineering, 2007-08-23)
      We develop a machine learning algorithm which learns rules for classification from training examples in a graph representation. However, unlike most other such algorithms which use one graph for each example, ours allows ...