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Volume 21 Issue 2
Mar.  1999
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Xiong Hanchun, He Qianhua, Li Haizhou. AN EFFICIENT EM TRAINING ALGORITHM FOR PROBABILITY MAPPING NETWORKS[J]. Journal of Electronics & Information Technology, 1999, 21(2): 175-181.
Citation: Xiong Hanchun, He Qianhua, Li Haizhou. AN EFFICIENT EM TRAINING ALGORITHM FOR PROBABILITY MAPPING NETWORKS[J]. Journal of Electronics & Information Technology, 1999, 21(2): 175-181.

AN EFFICIENT EM TRAINING ALGORITHM FOR PROBABILITY MAPPING NETWORKS

  • Received Date: 1997-08-25
  • Rev Recd Date: 1998-08-15
  • Publish Date: 1999-03-19
  • An Expectation-Maximization(EM) training algorithm for estimating the parameters of a special Probability Mapping Network (PMN) structure which forms a multicatolog Bayes classifier is proposed in this paper. The structure of PMN is a four-layer Feedforward Neural Networks(FNN), where the Gaussian probability density function is realized as an internal node. In this way, the EM algorithm is extended to deal with supervised learning of a multicatolog of the neural network Gaussian classifier. The computational efficiency and the numerical stability of the training algorithm benefit from the well-established EM framework. The effectiveness of the proposed network architecture and its EM training algorithm are assessed by conducting two experiments.
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  • Lee A S, IGl R M. A Gaussian potential function network with hierarchically self-organizing learning. Neural Networks, 1991 4(1): 207-224.[2] Speht P F. Probabilistic neural networks. Neural Networks, 1990, 3(1): 109-118.[2]Ma Sheng, Ji Chuanyi, Farmer J. An efficient EM-based training algorithm for feedforward neural networks. Neural Networks, 1997, 10(2), 243-256.[4] Streit R L, Luginbahl T E Maximum likelihood training of probabilistic neural networks. IEEE Trans. on NN, 1994, NN-5(5): 764-783.[3]Traven G C. A neural network approach to statistical pattern classfication by semi-parametric estimation of probability density function. IEEE Trans. on NN, 1991, NN-2(3): 366-377.[4]Dempster A P, Laird N M, Rubin D R. Maximum likelihood from incomplete date via the EM algorithm. J. Royal Statiscal Sac.,Ser. B, 1977; 39(1): 1-38.[7] Liporace L A. Maximum likelilood estimation for multivariate observations of Markov sources. IEEE Trans. on IT, 1982, IT-28(5): 729-734.[8] Wu J, Chan C. Isolated word recogontion by neural network models with crosscorrelation coef- ficient for speech recognition. IEEE Trans. on PAMI, 1993, PAMI-15(11): 1174-1185.
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