MUpstart-A constructive neural network learning algorithm for multi-category pattern classification

Rajesh Parekh, Jihoon Yang, Vasant Honavar

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

8 Scopus citations

Abstract

Constructive learning algorithms offer an approach for dynamically constructing near-minimal neural network architectures for pattern classification tasks. Several such algorithms proposed in the literature are shown to converge to zero classification errors on finite non-contradictory datasets. However, these algorithms are restricted to two-category pattern classification and (in most cases) they require the input patterns to have binary (or bipolar) valued attributes only. We present a provably correct extension of the upstart algorithm to handle multiple output classes and real-valued pattern attributes. Results of experiments with several artificial and real-world datasets demonstrate the feasibility of this approach in practical pattern classification tasks, and also suggest several interesting directions for future research.

Original languageEnglish (US)
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages1924-1929
Number of pages6
DOIs
StatePublished - Dec 1 1997
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume3
ISSN (Print)1098-7576

Other

Other1997 IEEE International Conference on Neural Networks, ICNN 1997
CountryUnited States
CityHouston, TX
Period6/9/976/12/97

All Science Journal Classification (ASJC) codes

  • Software

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    Parekh, R., Yang, J., & Honavar, V. (1997). MUpstart-A constructive neural network learning algorithm for multi-category pattern classification. In 1997 IEEE International Conference on Neural Networks, ICNN 1997 (pp. 1924-1929). [614193] (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 3). https://doi.org/10.1109/ICNN.1997.614193