DOI QR코드

DOI QR Code

Design of Polynomial Neural Network Classifier for Pattern Classification with Two Classes

  • 발행 : 2008.03.01

초록

Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.

참고문헌

  1. C.-L. Liu and H. Sako, 'Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition', Pattern Recognition, vol. 39, pp. 669-681, 2006 https://doi.org/10.1016/j.patcog.2005.04.021
  2. G.E. Hinton, P. Dayan and M. Revow, 'Modeling the manifolds of images of handwritten digits', IEEE Trans. Neural Networks, vol. 8, no. 1, pp. 65-74, 1997 https://doi.org/10.1109/72.554192
  3. J. Shurmann, Pattern Classification: A Unified View of Statistical and Neural Approaches, Wiley Interscience, New York, 1996
  4. U. KreBel and J. Schurmann, 'Pattern classification techniques based on function approximation', in Handbook of Character Recognition and Document Image Analysis, H. Bunke and P.S.P. Wang, Eds. World Scientific, Singapore, pp. 49?78, 1997
  5. R. P. Lippman, 'An introduction to computing with neural nets', IEEE ASSP Magazine, vol.4, no. 2, pp. 4-22, 1981
  6. A. Patrikar and J. Provence, 'Pattern classification using polynomial networks', Electronics Letters, vol. 28, no. 12, pp. 1109-1110, 1992 https://doi.org/10.1049/el:19920700
  7. A. Esposito, M. Marinaro, D. Oricchio and S. Scarpetta, 'Approximation of continuous and discontinuous mappings by a growing neural RBFbased algorithm', Neural Networks, vol. 13, no. 6, pp. 651-665, 2000 https://doi.org/10.1016/S0893-6080(00)00035-6
  8. F. Ros, M. Pintore and J.R. Chretien, 'Automatic design of growing radial basis function neural networks based on neighborhood concepts', Chemometrics and Intelligent Laboratory Systems, vol. 87, pp. 231-240, 2007 https://doi.org/10.1016/j.chemolab.2007.02.003
  9. H. Sarimveis, P. Doganis and A. Alexandridis, 'A classification technique based on radial basis function neural networks', Advances in Engineering Software, vol. 37, pp. 218-221, 2006 https://doi.org/10.1016/j.advengsoft.2005.07.005
  10. X.-Y. Jing, Y.-F. Yao, D. Zhang, J.-Y. Yang and M. Li, 'Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition', Pattern Recognition, vol. 40, pp. 3209-3224, 2007 https://doi.org/10.1016/j.patcog.2007.01.034
  11. M.J. Er, S.Q. Wu, J.W. Lu and H.L. Toh, 'Face recognition with radical basis function (RBF) neural networks', IEEE Trans. Neural Networks, vol. 13, no. 5, pp. 697-710, 2002 https://doi.org/10.1109/TNN.2002.1000134
  12. W.M. Campbell, K.T. Assaleh and C.C. Broun, 'Speaker recognition with polynomial classifiers', IEEE Trans. Speech and Audio Processing, vol. 10, no. 4, pp.205-212, 2002 https://doi.org/10.1109/TSA.2002.1011533
  13. L. Devroye, L. Gyorfi and G. Lugosi, A Probabilistic Theory of Pattern Recognition, Springer-Verlag, 1996
  14. W. Rudin, Principles of mathematical analysis, McGraw-Hill, 1976
  15. G.L. Giles and T. Maxwell, 'Learning, invariance, and generalization in high-order neural networks', Appl. Opt., vol. 26, no. 23, pp. 4972-4978, 1987 https://doi.org/10.1364/AO.26.004972
  16. Y.H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison -Wesley, Reading, MA, 1989
  17. Y. Al-Assaf and H. El Kadi, 'Fatigue life prediction of composite materials using polynomial classifiers and recurrent neural networks', Composite Structures, vol. 77, pp. 561-569, 2007 https://doi.org/10.1016/j.compstruct.2005.08.012
  18. C. Zhang, J. Jiang and M. Kamel, 'Intrusion detection using hierarchical neural networks', Pattern Recognition Letters, vol. 26, pp. 779-791, 2005 https://doi.org/10.1016/j.patrec.2004.09.045
  19. A. Aiyer, K. Pyun, Y.Z. Huang, D.B. O'Brien and R.M. Gray, 'Lloyd clustering of Gauss mixture models for image compression and classification', Signal Processing: Image Communication, vol. 20, pp. 459-485, 2005 https://doi.org/10.1016/j.image.2005.03.003
  20. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, N. York, 1981
  21. S.-K. Oh, W. Pderycz and B.-J. Park, 'Self-organizing neurofuzzy networks in modeling software data', Fuzzy Sets and Systems, vol. 145, pp. 165-181, 2004 https://doi.org/10.1016/j.fss.2003.10.009
  22. R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification, $2^nd$ ed., Wiley-Interscience, 2000
  23. J.E. Munoz-Exposito, S. Garcia-Galan, N. Ruiz-Reyes, P. Vera-Candeas, 'Adaptive network-based fuzzy inference system vs. other classification algorithms for warped LPC-based speech/music discrimination', Engineering Applications of Artificial Intelligence, vol. 20, pp. 783-793, 2007 https://doi.org/10.1016/j.engappai.2006.10.007

피인용 문헌

  1. Fuzzy set-oriented neural networks based on fuzzy polynomial inference and dynamic genetic optimization vol.39, pp.1, 2014, https://doi.org/10.1007/s10115-012-0610-x
  2. Exploring polynomial classifier to predict match results in football championships vol.83, 2017, https://doi.org/10.1016/j.eswa.2017.04.040
  3. Classification of masses in mammographic image using wavelet domain features and polynomial classifier vol.40, pp.15, 2013, https://doi.org/10.1016/j.eswa.2013.04.036
  4. Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization vol.163, pp.1, 2011, https://doi.org/10.1016/j.fss.2010.08.007
  5. Optimized face recognition algorithm using radial basis function neural networks and its practical applications vol.69, 2015, https://doi.org/10.1016/j.neunet.2015.05.001
  6. Design of face recognition algorithm using PCA -LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks : Design and its application vol.40, pp.5, 2013, https://doi.org/10.1016/j.eswa.2012.08.046
  7. Design of Optimized pRBFNNs-based Night Vision Face Recognition System Using PCA Algorithm vol.50, pp.1, 2013, https://doi.org/10.5573/ieek.2013.50.1.225