The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
An Efficient Sequential Learning Algorithm for Growing and Pruning RBF (GAP-RBF) Networks
Abstract
This paper presents a simple sequential growing and pruning algorithm for radial basis function (RBF) networks. The algorithm referred to as growing and pruning (GAP)-RBF uses the concept of "Significance" of a neuron and links it to the learning accuracy. "Significance" of a neuron is defined as its contribution to the network output averaged over all the input data received so far. Using a piecewise-linear approximation for the Gaussian function, a simple and efficient way of computing this significance has been derived for uniformly distributed input data. In the GAP-RBF algorithm, the growing and pruning are based on the significance of the "nearest" neuron. In this paper, the performance of the GAP-RBF learning algorithm is compared with other well-known sequential learning algorithms like RAN, RANEKF, and MRAN on an artificial problem with uniform input distribution and three real-world nonuniform, higher dimensional benchmark problems. The results indicate that the GAP-RBF algorithm can provide comparable generalization performance with a considerably reduced network size and training time.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
A Resource-Allocating Network for Function Interpolation
Platt, J. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.213 · Cited by 1,354
A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation
Huang, G.-B., Saratchandran, P., & Sundararajan, N. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.836241 · Cited by 705
Self-citationSkeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment
Mozer, M. C., & Smolensky, P. (1988) · neural information processing systems · Cited by 540
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
Puskorius, G. V., & Feldkamp, L. A. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279191 · Cited by 528
Yingwei, L., Sundararajan, N., & Saratchandran, P. (1997) · Neural Computation · doi:10.1162/neco.1997.9.2.461 · Cited by 507
Self-citationA Function Estimation Approach to Sequential Learning with Neural Networks
Kadirkamanathan, V., & Niranjan, M. (1993) · Neural Computation · doi:10.1162/neco.1993.5.6.954 · Cited by 458
Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.661125 · Cited by 449
Self-citationUsing Relevance to Reduce Network Size Automatically
MOZER, M. C., & SMOLENSKY, P. (1989) · Connection Science · doi:10.1080/09540098908915626 · Cited by 339
Cited by
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C.-K. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614
Self-citationExtreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,914
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Metadata
- Authors
- Huang, G.-B., Saratchandran, P., & Sundararajan, N.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2004-12
- Volume
- 34
- Issue
- 6
- Pages
- 2284-2292
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2131246644
- Cited by
- 391
- References
- 12
- FWCI
- 16.61
- Type
- journal article
- Language
- en
- Open access
- closed
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