The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Discriminative learning for minimum error classification (pattern recognition)
Abstract
A formulation is proposed for minimum-error classification, in which the misclassification probability is to be minimized based on a given set of training samples. A fundamental technique for designing a classifier that approaches the objective of minimum classification error in a more direct manner than traditional methods is given. The method is contrasted with several traditional classifier designs in typical experiments to demonstrate the superiority of the new learning formulation. The method can applied to other classifier structures as well. Experimental results pertaining to a speech recognition task are provided to show the effectiveness of the technique.>
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Citations
Cites
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
McClelland, J. L., Rumelhart, D. E., & AU (1987) · doi:10.7551/mitpress/5237.001.0001 · Cited by 3,315
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002
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
Ensemble of feature sets and classification algorithms for sentiment classification
Xia, R., Zong, C., & Li, S. (2011) · Information Sciences · doi:10.1016/j.ins.2010.11.023 · Cited by 583
Handwritten digit recognition: benchmarking of state-of-the-art techniques
Liu, C.-L., Nakashima, K., Sako, H., & Fujisawa, H. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00085-2 · Cited by 543
Contrastive estimation: training log-linear models on unlabeled data
Smith, N. A., & Eisner, J. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219884 · Cited by 305
Hidden conditional random fields for phone classification
Gunawardana, A., Mahajan, M., Acero, A., & Platt, J. C. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-126 · Cited by 299
Metadata
- Authors
- Juang, B.-H., & Katagiri, S.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1992
- Volume
- 40
- Issue
- 12
- Pages
- 3043-3054
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2063541597
- Cited by
- 714
- References
- 3
- FWCI
- 10.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Discriminative learning for minimum error classification (pattern recognition). https://app.cochat.ai/papers/10.1109/78.175747
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- Juang, B.-H., & Katagiri, S. (1992). Discriminative learning for minimum error classification (pattern recognition). IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.175747
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
