TRs available

[Jai Choi]

To whom it may concern:

We appreciate if you post followings which advertises two
technical notes.  Thanks in advance.
		Jai Choi.


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		 Two Technical Notes Available
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1.      Query Learning Based on Boundary Search and Gradient 
           Computation of Trained Multilayer Perceptrons

     Jenq-Neng Hwang,  Jai J. Choi,  Seho Oh, Robert J. Marks II 

                 Interactive Systems Design Lab.
              Department of Electrical Engr., FT-10 
                    University of Washington
                       Seattle, WA 98195


	            ****** Abstract *******

In many machine learning applications, the source of the training data 
can be modeled as an oracle.  An oracle has the ability, when presented 
with an example (query), to give a correct classification. An efficient
query learning is to  provide the good training data to the oracle at low
cost. This report presents a novel approach for query based neural
network learning.  Consider a layered perceptron partially trained for 
binary classification.  The single output neuron is trained to be either 
a 0 or a 1. A test decision is made by thresholding the output at, say,
0.5.  The set of inputs that produce an output of 0.5, forms the classification
boundary.  We adopted an inversion algorithm for the neural network that
allows generation of this boundary.  In addition, for each boundary point, 
we can generate the classification gradient.  The gradient provides a useful
measure of the sharpness of the multi-dimensional decision surfaces.  Using
the boundary point and gradient information, conjugate input pair locations 
are generated and presented to an oracle for proper classification.  
This new data is used to further refine the classification boundary thereby
increasing the classification accuracy.  The result can be a significant 
reduction in the training set cardinality in comparison with, for example,
randomly generated data points.  An application example to  power security 
assessment is given.
             (will be presented in IJCNN'90,  San Diego)

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2. Iterative Constrained Inversion of Neural Networks and its Applications

               Jenq-Neng Hwang, Chi H. Chan 

		****** Abstract ******
  
This report presents a new approach to solve the constrained inverse problems
for a trained nonlinear mapping.  These problems can be found in a wide variety
of applications in dynamic control of nonlinear systems and nonlinear 
constrained optimization.  The forward problem in a nonlinear functional 
mapping is to obtain the best approximation of the output vector given the 
input vector.  The inverse problem,  on the other hand, is to obtain the best
approximation of the input vector given a specified output vector, i.e., to 
find the inverse function of the nonlinear mapping, which might not exist 
except when the constraints are imposed on. 
Most neural networks previously proposed for training the inverse mapping 
either  adopted an one-way constraint perturbation or a two-stage learning.
Both of these approaches are very laborious and unreliable.
Instead of using two  neural networks for emulating the forward
and inverse mappings separately, we applied the network inversion algorithm,
which works directly on the network used to train the forward mapping, yielding
the inverse mapping.  Our approach uses one network to emulate both of forward
and inverse nonlinear mapping without explicitly characterizing and
implementing the inverse mapping.  Furthermore, our single network inversion
approach  allows to iteratively locate the optimal inverted solution which 
also satisfies some  constraints imposed on the inputs, and also allows best 
exploitation of the sensitivity measure of the inputs to outputs in a  non-
linear mapping.
 (presented in 24 Conf. on Information Systems and Sciences)


********  For copy of above two TR ************

Send your physical address to 

	Jai Choi
	Dept. EE, FT-10
	Univ. of Washington
	Seattle, WA 98195

or  "jai at blake.acs.washington.edu".