TRs available.

[Ke CHEN]

The following two papers are available on line now.

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  Combining Linear Discriminant Functions with Neural Networks
                   for Supervised Learning

       Ke Chen{1,2}, Xiang Yu {2} and Huisheng Chi {2}

  {1} Dept. of CIS and Center for Cognitive Science, Ohio State U.
  {2} National Lab of Machine Perception, Peking U., China

   appearing in Neural Computing & Applications 6(1): 19-41, 1997.

                           ABSTRACT

A novel supervised learning method is presented by combining linear
discriminant functions with neural networks. The proposed method results
in a tree-structured hybrid architecture.  Due to constructive learning,
the binary tree hierarchical architecture is automatically generated by a
controlled growing process for a specific supervised learning task. 
Unlike the classic decision tree, the linear discriminant functions are
merely employed in the intermediate level of the tree for heuristically
partitioning a large and complicated task into several smaller and simpler
subtasks in the proposed method. These subtasks are dealt with by
component neural networks at the leaves of the tree accordingly.  For
constructive learning, {\it growing} and {\it credit-assignment}
algorithms are developed to serve for the hybrid architecture. The
proposed architecture provides an efficient way to apply existing neural
networks (e.g.  multi-layered perceptron) for solving a large scale
problem.  We have already applied the proposed method to a universal
approximation problem and several benchmark classification problems in
order to evaluate its performance.  Simulation results have shown that the
proposed method yields better results and faster training in comparison
with the multi-layered perceptron. 

URL: http://www.cis.ohio-state.edu/~kchen/nca.ps
      ftp://www.cis.ohio-state.edu/~kchen/nca.ps
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   Methods of Combining Multiple Classifiers with Different Features
   and Their Applications to Text-Independent Speaker Identification

       Ke Chen{1,2}, Lan Wang {2} and Huisheng Chi {2}

  {1} Dept. of CIS and Center for Cognitive Science, Ohio State U.
  {2} National Lab of Machine Perception, Peking U., China

     appearing in International Journal of Pattern Recognition
      and Artificial Intelligence 11(3): 417-445, 1997.

                           ABSTRACT

In practical applications of pattern recognition, there are often
different features extracted from raw data which needs recognizing. 
Methods of combining multiple classifiers with different features are
viewed as a general problem in various application areas of pattern
recognition. In this paper, a systematic investigation has been made and
possible solutions are classified into three frameworks, i.e. linear
opinion pools, winner-take-all and evidential reasoning. For combining
multiple classifiers with different features, a novel method is presented
in the framework of linear opinion pools and a modified training algorithm
for associative switch is also proposed in the framework of
winner-take-all.  In the framework of evidential reasoning, several
typical methods are briefly reviewed for use.  All aforementioned methods
have already been applied to text-independent speaker identification.  The
simulations show that results yielded by the methods described in this
paper are better than not only the individual classifiers' but also ones
obtained by combining multiple classifiers with the same feature. It
indicates that the use of combining multiple classifiers with different
features is an effective way to attack the problem of text-independent
speaker identification. 

URL: http://www.cis.ohio-state.edu/~kchen/ijprai.ps
      ftp://www.cis.ohio-state.edu/~kchen/ijprai.ps
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----------------------------------------------------
Dr. Ke CHEN
Department of Computer and Information Science
The Ohio State University
583 Dreese Laboratories
2015 Neil Avenue
Columbus, Ohio 43210-1277, U.S.A.
E-Mail: kchen at cis.ohio-state.edu
WWW: http://www.cis.ohio-state.edu/~kchen
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