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Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #306084 |
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Title:
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A Nonparametric Approach Based on a like Markov Property for Classification
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Author(s):
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Eun Sug Park*+ and Clifford Spiegelman
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Companies:
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Texas Transportation Institute and Texas A&M University
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Address:
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, College Station, TX, 77843-3135,
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Keywords:
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classification ; high dimensional data
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Abstract:
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We suggest a new approach for classification based on nonparametricly estimated likelihoods. Due to the scarcity of data in high dimensions, nonparametric estimation of the likelihood functions for each population is impractical. Instead, we propose to build a class of candidate likelihood models based on a Markov property and to provide multiple likelihood estimates that are useful for guiding a classification algorithm. Our density estimates require only estimates of one and two-dimensional marginal distributions, which can effectively get around the curse of dimensionality problem. A classification algorithm based on those estimated likelihoods is presented. A modification to it utilizing variable selection of differences in log of estimated marginal densities is also suggested to handle specifically high dimensional data.
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