BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-97-1587
       ENTRY:: March 24, 1997
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Ensembles for Supervised Classification Learning
        TYPE:: Thesis
        TYPE:: Technical Report
      AUTHOR:: Matan, Ofer
        DATE:: March 1997
       PAGES:: 100
    ABSTRACT:: This dissertation studies the use of multiple classifiers
               (ensembles or committees) in learning tasks. Both theoretical
               and practical aspects of combining classifiers are studied.

               First we analyze the representational ability of voting
               ensembles. A voting ensemble may perform either better or
               worse than each of its individual members. We give tight
               upper and lower bounds on the classification performance of a
               voting ensemble as a function of the classification
               performances of its individual members.

               Boosting is a method of combining multiple "weak"
               classifiers to form a "strong" classifier. Several issues
               concerning boosting are studied in this thesis. We study SBA,
               a hierarchical boosting algorithm proposed by Schapire, in
               terms of its representation and its search. We present a
               rejection boosting algorithm that trades-off exploration and
               exploitation: It requires fewer pattern labels at the expense
               of lower boosting ability.

               Ensembles may be useful in gaining information. We study
               their use to minimize labeling costs of data and to enable
               improvements on performance over time. For that purpose a
               model for on-site learning is presented. The system learns by
               querying "hard" patterns while classifying "easy" ones.
       NOTES:: [Adminitrivia V1/Prg/19970324]
         END:: STAN//CS-TR-97-1587