Yang, Jihoon and Honavar, Vasant G. (1997) DistAl: An Inter-pattern Distance-based Constructive Learning Algorithm. Technical Report TR97-05, Department of Computer Science, Iowa State University.
DistAl: An Inter-pattern Distance-based Constructive
Jihoon Yang, Rajesh Parekh and Vasant Honavar
Artificial Intelligence Research Group
Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, IA 50011. U.S.A.
Multi-layer networks of threshold logic units offer an attractive framework
for the design of pattern classification systems.
A new constructive neural network learning algorithm (DistAl)
based on inter-pattern distance is introduced.
DistAl uses spherical threshold neurons in a hidden
layer to find a cluster of patterns to be covered (or classified)
by each hidden neuron.
It does not depend on an iterative, expensive and time-consuming
perceptron training algorithm
to find the weight settings for the neurons in the network,
and thus extremely fast even for large data sets.
The experimental results (in terms of generalization capability
and network size)
of DistAl on a number of benchmark classification problems
show reasonable performance compared to other learning algorithms
despite its simplicity and fast learning time.
Therefore, DistAl is a good candidate to various tasks
that involve very large data sets
(such as largescale datamining and knowledge acquisition) or
that require reasonably accurate classifiers to be learned in almost
real time or that use neural network learning as the inner loop of a more
complex optimization process in hybrid learning systems.
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