Constructive neural network learning algorithms

Rajesh Parekh, Jihoon Yang, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Constructive algorithms can increase the construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms have a set of inductive and representational biases implicit in the design choices that determine where to add neurons and how to train and prune them. The biases that best suit the needs of each individual classification tasks are then systematically characterized.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Editors Anon
PublisherAAAI
Pages1398
Number of pages1
Volume2
StatePublished - 1996
EventProceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) - Portland, OR, USA
Duration: Aug 4 1996Aug 8 1996

Other

OtherProceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2)
CityPortland, OR, USA
Period8/4/968/8/96

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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  • Cite this

    Parekh, R., Yang, J., & Honavar, V. (1996). Constructive neural network learning algorithms. In Anon (Ed.), Proceedings of the National Conference on Artificial Intelligence (Vol. 2, pp. 1398). AAAI.