Abstract
Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is introduced. The MLPNN uses an incremental constructive approach, i.e. it grows level by level. The construction algorithm of the MLPNN is proposed such that the classification accuracy monotonically increases to ensure that the classification accuracy of the MLPNN is higher than or equal to that of the traditional PNN. Numerical examples are included to demonstrate the effectiveness of proposed new approach.
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References
Specht, D.F.: Probilistic Neural Networks. Neural Networks 3, 109–118 (1990)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Mao, K.Z., Tan, K.C., Ser, W.: Probabilistic Neural-network Structure Determination for Pattern Classification. IEEE Transactions on Neural Networks 3, 1009–1016 (2000)
Specht, D.F.: Enhancements to The Probabilistic Neural Networks. In: Proc IEEE Int. Conf. Neural Networks, Baltimore, MD, pp. 761–768 (1992)
Zaknich, A.: A Vector Quantization Reduction Method for The Probabilistic Neural Networks. In: Proc IEEE Int. Conf. Neural Networks, Piscataway, NJ (1997)
Zong, N.: Data-based Models Design and Learning Algorithms for Pattern Recognition. PhD thesis, School of Systems Engineering, University of Reading, UK (2006)
Breiman, L.: Arcing Classifiers. Annals of Statistics 26, 801–849 (1998)
Gelfand, S.B., Ravishankar, C.S., Delp, E.J.: Tree-structured Piecewise Linear Adaptive Equalization. IEEE Trans. on Communications 41, 70–82 (1993)
Billings, S.A., Voon, W.S.F.: Piecewise Linear Identificaiton of Nonlinear Systems. International Journal of Control 46, 215–235 (1987)
Sklansky, J., Michelotti, L.: Locally Trained Piecewise Linear Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 2, 101–111 (1980)
Zong, N., Hong, X.: On Improvement of Classification Accuracy for Stochastic Discrimination- multi-class Classification. In: Proc. of Int. Conf. on Computing, Communications and Control Technologies, CCCT’04, vol. 3, pp. 109–114 (2004)
Zong, N., Hong, X.: On Improvement of Classification Accuracy for Stochastic Discrimination. IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics 35, 142–149 (2005)
Kleinberg, E.M.: Stochastic Discrimination. Annals of Mathematics and Artificial Intelligence 1, 207–239 (1990)
Kleinberg, E.M.: An Overtraining-resistant Stochastic Modeling Method for Pattern Recognition. Annals of Statistics 24, 2319–2349 (1996)
Kleinberg, E.M.: On The Algorithmic Implementation of Stochastic Discrimination. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 473–490 (2000)
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Zong, N., Hong, X. (2007). A Multi-Level Probabilistic Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_62
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DOI: https://doi.org/10.1007/978-3-540-72393-6_62
Publisher Name: Springer, Berlin, Heidelberg
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