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        <identifier>oai.jstatsoft/v17/i02</identifier>
        <datestamp>2006-09-26</datestamp>
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          <dc:title>ada: An R Package for Stochastic Boosting</dc:title>
          <dc:creator>Mark Culp</dc:creator>
          <dc:creator>Kjell Johnson</dc:creator>
          <dc:creator>George Michailides</dc:creator>
          <dc:date>2006-09-26</dc:date>
          <dc:publisher>Journal of Statistical Software</dc:publisher>
          <dc:description>
            <![CDATA[Vol. 17, Issue 2, Sep 2006
Abstract:
Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.]]>
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          <dc:subject>Statistics</dc:subject>
          <dc:subject>Software</dc:subject>
          <dc:identifier>http://www.jstatsoft.org/v17/i02</dc:identifier>
          <dc:source>http://www.jstatsoft.org/v17/i02/paper</dc:source>
          <dc:type>Text</dc:type>
          <dc:type>Software</dc:type>
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