Now showing items 1-8 of 8

  • A fast seeding technique for k-means algorithm. 

    Karbasi, Seyedeh Paniz. 1986- (2014-11-06)
    The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing ...
  • Age classification from facial images for detecting retinoblastoma. 

    Chiam, Tak Chien. (, 2012-11-29)
    Facial age estimation from images is a difficult problem, both because it is naturally difficult to tell the exact age of a person visually, and because of the variations in images, such as illumination, pose, and expression. ...
  • Clustering in high dimension and choosing cluster representatives for SimPoint. 

    Johnston, Joshua Benjamin. (2007-12-03)
    In computer architecture, researchers compare new processor designs by simulating them in software. Because simulation is slow, researchers simulate small parts of a workload to save time. The widely successful SimPoint ...
  • Faster k-means clustering. 

    Drake, Jonathan, 1989- (, 2013-09-24)
    The popular k-means algorithm is used to discover clusters in vector data automatically. We present three accelerated algorithms that compute exactly the same clusters much faster than the standard method. First, we redesign ...
  • Hierarchical stability based model selection for clustering algorithms. 

    Yin, Bing, 1985- (2009-09-09)
    We present an algorithm called HS-means, which is able to learn the number of clusters in a mixture model based on the hierarchical analysis of clustering stability. Our method extends the concept of clustering stability ...
  • Information storage capacity of genetic algorithm fitness maps. 

    Montañez, George D. (, 2011-09-14)
    To accurately measure the amount of information a genetic algorithm can generate, we must first measure the amount of information one can store, using a fitness map. The amount of information generated, minus the storage ...
  • PG-means: learning the number of clusters in data. 

    Feng, Yu. (2007-03-19)
    We present a novel algorithm called PG-means in this thesis. This algorithm is able to determine the number of clusters in a classical Gaussian mixture model automatically. PG-means uses efficient statistical hypothesis ...
  • Studies of active information in search. 

    Ewert, Winston. (2010)
    A search process is an attempt to locate a solution to a problem, such as an optimization problem, where the space is usually too large to exhaustively sample. In order to investigate this idea this work looks a three ...