The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Statistical pattern recognition with neural networks: benchmarking studies
Abstract
Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's approach to solving the tasks was evaluated and compared, both to the performance of the other two networks and to the theoretical limit. The learning vector quantization was further benchmarked against the parametric Bayes classifier and the k-nearest-neighbor classifier using natural speech data. A novel learning vector quantization classifier called LVQ2 is introduced.>
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Cites
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Cited by
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Engineering applications of the self-organizing map
Kohonen, T., Oja, E., Simula, O., Visa, A., & Kangas, J. (1996) · Proceedings of the IEEE · doi:10.1109/5.537105 · Cited by 847
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Using additive noise in back-propagation training
Holmstrom, L., & Koistinen, P. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.105415 · Cited by 355
Metadata
- Authors
- Kohonen, Barna, & Chrisley
- Venue
- IEEE International Conference on Neural Networks
- Publisher
- IEEE
- Published
- 1988
- Pages
- 61-68 vol.1
- ISSN
- 1098-7576 (ISSN-L)
- OpenAlex
- W1761621746
- Cited by
- 352
- References
- 12
- FWCI
- 16.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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