The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Decision-based neural networks with signal/image classification applications
Abstract
Supervised learning networks based on a decision-based formulation are explored. More specifically, a decision-based neural network (DBNN) is proposed, which combines the perceptron-like learning rule and hierarchical nonlinear network structure. The decision-based mutual training can be applied to both static and temporal pattern recognition problems. For static pattern recognition, two hierarchical structures are proposed: hidden-node and subcluster structures. The relationships between DBNN's and other models (linear perceptron, piecewise-linear perceptron, LVQ, and PNN) are discussed. As to temporal DBNN's, model-based discriminant functions may be chosen to compensate possible temporal variations, such as waveform warping and alignments. Typical examples include DTW distance, prediction error, or likelihood functions. For classification applications, DBNN's are very effective in computation time and performance. This is confirmed by simulations conducted for several applications, including texture classification, OCR, and ECG analysis.
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Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
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
PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS
ROSENBLATT, F. (1961) · doi:10.21236/ad0256582 · Cited by 1,691
Cited by
Artificial neural networks as a tool in ecological modelling, an introduction
Lek, S., & Guégan, J. F. (1999) · Ecological Modelling · doi:10.1016/s0304-3800(99)00092-7 · Cited by 889
Recent advances in visual and infrared face recognition—a review
Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.04.001 · Cited by 586
Face recognition/detection by probabilistic decision-based neural network
Lin, S.-H., Kung, S.-Y., & Lin, L.-J. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554196 · Cited by 578
Self-citationFace recognition using line edge map
Gao, Y., & Leung, M. K. H. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1008383 · Cited by 489
One-lead ECG for identity verification
Shen, T. W., Tompkins, W. J., & Hu, Y. H. (2003) · Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology · doi:10.1109/iembs.2002.1134388 · Cited by 278
Metadata
- Authors
- Kung, S. Y., & Taur, J. S.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1995
- Volume
- 6
- Issue
- 1
- Pages
- 170-181
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2159540571
- Cited by
- 164
- References
- 3
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kung, S. Y., & Taur, J. S. (1995). Decision-based neural networks with signal/image classification applications. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.363439
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