Constructive neural-network learning algorithms for pattern classification

@article{Parekh2000ConstructiveNL,
  title={Constructive neural-network learning algorithms for pattern classification},
  author={Rajesh Parekh and Jihoon Yang and Vasant Honavar},
  journal={IEEE transactions on neural networks},
  year={2000},
  volume={11 2},
  pages={436-51}
}
Constructive learning algorithms offer an attractive approach for the incremental construction of near-minimal neural-network architectures for pattern classification. They help overcome the need for ad hoc and often inappropriate choices of network topology in algorithms that search for suitable weights in a priori fixed network architectures. Several such algorithms are proposed in the literature and shown to converge to zero classification errors (under certain assumptions) on tasks that… CONTINUE READING
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Experiments with the cascade-correlation algorithm

J. Yang, V. Honavar
Microcomput. Applicat. , vol. 17, no. 2, pp. 40–46, 1998. PAREKH et al.: CONSTRUCTIVE NEURAL-NETWORK LEARNING ALGORITHMS FOR PATTERN CLASSIFICATION 451 • 1998
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