H.-C. Yau and M. T. Manry, "Iterative improvement of a nearest neighbor classifier," Neural Networks, vol. 4, pp. 517--524, 1991. Home/Search Document Details and Download
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Presupervised and Postsupervised Prototype Classifier Design - Kuncheva, Bezdek (Correct)
.... (RBF) networks Research supported in parts by the NRC COBASE program and ONR Grant # N 00014 96 1 0642 [5] 19] 23] a class of fuzzy if then systems [15] Learning Vector Quantization (LVQ) classi ers [11] 12] 14] edited nearest neighbor rules [6] fuzzy nearest neighbor rules [1] 13] [26], multiple prototype classi ers [2] and a number of neural network implementations of the nearest neighbor design [7] 17] 26] Each of these has speci c strategies and algorithms for nding the prototypes. The diagram in Figure 1 groups the GNPCs into presupervised and postsupervised designs ....
.... fuzzy if then systems [15] Learning Vector Quantization (LVQ) classi ers [11] 12] 14] edited nearest neighbor rules [6] fuzzy nearest neighbor rules [1] 13] 26] multiple prototype classi ers [2] and a number of neural network implementations of the nearest neighbor design [7] 17] [26]. Each of these has speci c strategies and algorithms for nding the prototypes. The diagram in Figure 1 groups the GNPCs into presupervised and postsupervised designs with respect to the way the prototypes are found and labeled. In each group we distinguish GNPCs with crisp and noncrisp (soft) ....
H.-C. Yau and M.T. Manry, \Iterative improvement of a nearest neighbor classier," Neural Networks, vol. 4, pp. 517-424, 1991. 10
Monomial Activation Functions For The Multi-Layer Perceptron - Olvera (1992) (1 citation) Self-citation (Manry) (Correct)
.... approaches to the Bayes Gaussian classifier are the nearest neighbor classifiers (NNC) and artificial neural networks (ANN) In practical applications, the NNC is often applied because it does not require an a priori knowledge of the joint probability density of the input feature vectors [4]. The error probability of the NNC approaches that of the Bayes classifier as the number of example vectors increases. However, two problems exist when applying the NNC classifier. One is the prohibitive amount of computation required for its use. The second is that for a small number of example ....
H. C. Yau and M. T. Manry, "Iterative Improvement of a Nearest Neighbor Classifier," Neural Networks, Vol. 4, pp. 517-524, 1991.
Predicting the Generalization Ability of Neural Networks.. - Muselli (2000) (Correct)
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H.-C. Yau and M. T. Manry, "Iterative improvement of a nearest neighbor classifier," Neural Networks, vol. 4, pp. 517--524, 1991.
An Integrated Framework For Generalized Nearest Prototype.. - Kuncheva, Bezdek (1998) (1 citation) (Correct)
No context found.
H.-C. Yau and M.T. Manry. \Iterative improvement of a nearest neighbor classier," Neural Networks, vol. 4, pp. 517-424, 1991.
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