Experiments on Brood Size in GP with Brood Recombination Crossover for Object Recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.4020

  author =       "Mengjie Zhang and Xiaoying Gao and Minh Duc Cao",
  title =        "Experiments on Brood Size in GP with Brood
                 Recombination Crossover for Object Recognition",
  institution =  "Computer Science, Victoria University of Wellington",
  year =         "2006",
  number =       "CS-TR-06-6",
  address =      "New Zealand",
  keywords =     "genetic algorithms, genetic programming, Document
                 Classification, Baysian Networks, Citation Links",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-6.abs.html",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-6.pdf",
  abstract =     "citation links to improve the scientific paper
                 classification performance. In this approach, we
                 develop two refinement functions, a linear label
                 refinement (LLR) and a probabilistic label refinement
                 (PLR), to model the citation link structures of the
                 scientific papers for refining the class labels of the
                 documents obtained by the content-based Naive Bayes
                 classification method. The approach with the two new
                 refinement models is examined and compared with the
                 content-based Naive Bayes method on a standard paper
                 classification data set with increasing training set
                 sizes. The results suggest that both refinement models
                 can significantly improve the system performance over
                 the content-based method for all the training set sizes
                 and that PLR is better than LLR when the training
                 examples are sufficient.",

Genetic Programming entries for Mengjie Zhang Xiaoying (Sharon) Gao Minh Duc Cao