Experiments on Brood Size in GP with Brood
Recombination Crossover for Object Recognition
M. Zhang, X. Gao, and M. Cao. CS-TR-06-6. Computer Science, Victoria University of Wellington, New Zealand, (2006)
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.
%0 Report
%1 vuw-CS-TR-06-6
%A Zhang, Mengjie
%A Gao, Xiaoying
%A Cao, Minh Duc
%C New Zealand
%D 2006
%K Baysian Citation Classification, Document Links Networks, algorithms, genetic programming,
%N CS-TR-06-6
%T Experiments on Brood Size in GP with Brood
Recombination Crossover for Object Recognition
%U http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-6.abs.html
%X 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.
@techreport{vuw-CS-TR-06-6,
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.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {New Zealand},
author = {Zhang, Mengjie and Gao, Xiaoying and Cao, Minh Duc},
biburl = {https://www.bibsonomy.org/bibtex/2c29743e42d27669e1e5d005706618c16/brazovayeye},
institution = {Computer Science, Victoria University of Wellington},
interhash = {96a432ff92db0712c7b51efbbc77eb7c},
intrahash = {c29743e42d27669e1e5d005706618c16},
keywords = {Baysian Citation Classification, Document Links Networks, algorithms, genetic programming,},
number = {CS-TR-06-6},
timestamp = {2008-06-19T17:55:37.000+0200},
title = {Experiments on Brood Size in {GP} with Brood
Recombination Crossover for Object Recognition},
url = {http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-6.abs.html},
year = 2006
}