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[John Pastor]

The following technical report is now available. If you would
like to have a copy, please let me know.

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Technical Report ECE/LSU 93-04

                 Another Alternative to Backpropagation: 
                A One Pass Classification Scheme for Use 
                         with the Kak algorithm 

                             John F. Pastor 
             Department of Electrical and Computer Engineering 
                       Louisiana State University 
                         Baton Rouge, La. 70803
                             April 26,1993
                      email: pastor at max.ee.lsu.edu

                                ABSTRACT
     Kak[1] provides a new technique for designing, and training, a 
feedforward neural network.  Training with the Kak algorithm is much
faster, and is implemented much more easily, than with the backpropagation
algorithm[2].  The Kak algorithm calls for the construction of a network
with one hidden layer.  Each hidden neuron classifies an input vector in
the training set that maps to a nonzero output vector.  Kak[1] also 
presents two classification algorithms.  The first, CC1, provides 
generalization comparable to backpropagation[2] but may require numerous
passes through the training set to classify one input vector.  The second,
CC2, only requires inspection of the vector we wish to classify but does
not provide generalization.  An extension of CC2 is suggested as a new
classification scheme that will classify an input vector with only one
pass through the training set yet will provide generalization.  Simulation
results are presented that demonstrate that using the new classification
scheme not only signifigantly reduces training time, but provides better
generalization capabilities, than classifying with CC1.  Thus, the Kak
algorithm, using this new classification scheme, is an even better
alternative to backpropagation.