Chen, C-H., Parekh, R., Yang, J., Balakrishnan, K. and Honavar, V. (1995) Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms. Technical Report TR95-12, Department of Computer Science, Iowa State University.
Analysis of Decision Boundaries Generated by Constructive
Neural Network Learning Algorithms}
C-H Chen, R. G. Parekh, J. Yang, K. Balakrishnan, & V. Honavar
Department of Computer Science
226 Atanasoff Hall,
Iowa State University,
Ames, IA 50011. U.S.A.
Constructive learning algorithms offer an approach to
incremental construction of near-minimal artificial neural networks
for pattern classification. Examples of such algorithms include
Tower, Pyramid, Upstart, and Tiling algorithms which construct multilayer
networks of threshold logic units (or, multi-layer perceptrons).
These algorithms differ in terms of the topology of the networks that
they construct which in turn biases the search for a decision boundary
that correctly classifies the training set. This paper presents an
analysis of such algorithms from a geometrical perspective. This analysis
helps in a better characterization of the search bias employed by the
different algorithms in relation to the geometrical distribution of
examples in the training set. Simple experiments with non linearly
separable training sets support the results of mathematical analysis of
such algorithms. This suggests the possibility of designing more
efficient constructive algorithms that dynamically choose among different
biases to build near-minimal networks for pattern classification.
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