4,458 research outputs found

    COMET: A Recipe for Learning and Using Large Ensembles on Massive Data

    Full text link
    COMET is a single-pass MapReduce algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed blocks of data and merges them into a mega-ensemble. This approach is appropriate when learning from massive-scale data that is too large to fit on a single machine. To get the best accuracy, IVoting should be used instead of bagging to generate the training subset for each decision tree in the random forest. Experiments with two large datasets (5GB and 50GB compressed) show that COMET compares favorably (in both accuracy and training time) to learning on a subsample of data using a serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble evaluation which dynamically decides how many ensemble members to evaluate per data point; this can reduce evaluation cost by 100X or more

    Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing

    Full text link
    People from all over the world use social media to share thoughts and opinions about events, and understanding what people say through these channels has been of increasing interest to researchers, journalists, and marketers alike. However, while automatically generated summaries enable people to consume large amounts of data efficiently, they do not provide the context needed for a viewer to fully understand an event. Narrative structure can provide templates for the order and manner in which this data is presented to create stories that are oriented around narrative elements rather than summaries made up of facts. In this paper, we use narrative theory as a framework for identifying the links between social media content. To do this, we designed crowdsourcing tasks to generate summaries of events based on commonly used narrative templates. In a controlled study, for certain types of events, people were more emotionally engaged with stories created with narrative structure and were also more likely to recommend them to others compared to summaries created without narrative structure

    Edge Routing with Ordered Bundles

    Full text link
    Edge bundling reduces the visual clutter in a drawing of a graph by uniting the edges into bundles. We propose a method of edge bundling drawing each edge of a bundle separately as in metro-maps and call our method ordered bundles. To produce aesthetically looking edge routes it minimizes a cost function on the edges. The cost function depends on the ink, required to draw the edges, the edge lengths, widths and separations. The cost also penalizes for too many edges passing through narrow channels by using the constrained Delaunay triangulation. The method avoids unnecessary edge-node and edge-edge crossings. To draw edges with the minimal number of crossings and separately within the same bundle we develop an efficient algorithm solving a variant of the metro-line crossing minimization problem. In general, the method creates clear and smooth edge routes giving an overview of the global graph structure, while still drawing each edge separately and thus enabling local analysis

    Spartan Daily, May 11, 1967

    Get PDF
    Volume 54, Issue 118https://scholarworks.sjsu.edu/spartandaily/4984/thumbnail.jp

    Portfolio, 1950

    Get PDF
    https://digitalcommons.risd.edu/archives_yearbooks/1057/thumbnail.jp

    Portfolio, 1949

    Get PDF
    https://digitalcommons.risd.edu/archives_yearbooks/1056/thumbnail.jp

    The Cowl - v.17 - n.5 - Nov 10, 1954

    Get PDF
    The Cowl - student newspaper of Providence College. Volume 17, Number 5 - Nov 10, 1954. 6 pages
    corecore