Generalized classifier neural network

Neural Networks

Volumn 39, Issue , 2013, Pages 18-26

  Ozyildirim, Buse Melis ^a   Avci, Mutlu ^b  

^a ADANA SCIENCE AND TECHNOLOGY UNIVERSITY   (Turkey)

^b CUKUROVA UNIVERSITY   (Turkey)

Author keywords

Classification neural networks; Gradient descent learning

Indexed keywords

CLASSIFICATION PERFORMANCE; DATA SETS; GENERALIZED REGRESSION NEURAL NETWORKS; GRADIENT DESCENT; MATLAB ENVIRONMENT; MULTI LAYER PERCEPTRON; OUTPUT LAYER; PROBABILISTIC NEURAL NETWORKS; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; SEPARATION ABILITY; SMOOTHING PARAMETER;

RADIAL BASIS FUNCTION NETWORKS; TWO PHASE FLOW;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; GENERALIZED CLASSFIER NEURAL NETWORK; GENERALIZED REGRESSION NEURAL NETWORK; MATHEMATICAL ANALYSIS; NONHUMAN; PRIORITY JOURNAL; PROBABILISTIC NEURAL NETWORK; RADIAL BASED FUNCTION;

ALGORITHMS; CLASSIFICATION; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 84872194773     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.12.001     Document Type: Article

References (35)

1. Adeli H., Panakkat A. A probabilistic neural network for earthquake magnitude prediction. Neural Networks 2009, 22:1018-1024.
2. Al-Daoud E. A comparison between three neural network models for classification problems. Journal of Artificial Intelligence 2009, 2:56-64.
3. Amrouche A., Rouvaen J.M. Efficient system for speech recognition using general regression neural network. International Journal of Computer Systems Science and Engineering 2006, 183-189.
4. Asad B., Zhijiang D., Lining S., Reza K., Fereidoun M.A. Fast 3D reconstruction of ultrasonic images based on generalized regression neural network. World congress on medical physics and biomedical engineering 2007.
5. Bartlett P.L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory 1998, 44(2):525-536.
6. Bennett K.P., Mangasarian O.L. Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1992, 23-34.
7. Berthold M.R., Diamond J. Constructive training of probabilistic neural networks. Neurocomputing 1998, 19:167-183.
8. Erkmen B., Yildirim T. Improving classification performance of sonar targets by applying general regression neural network with PCA. Expert Systems with Applications 2008, 35:472-475.
9. Firat M., Gungor M. Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software 2009, 40:731-737.
10. Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archieve.ics.uci.edu/ml.
11. Hajmeer M., Basheer I. A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. Journal of Microbiological Methods 2002, 51:217-226.
12. Hoya T., Chambers J.A. Heuristic pattern corrrection scheme using adaptively trained generalized regression neural networks. IEEE Transactions on Neural Networks 2001, 12:1.
13. Kailun, H., Huijun, X., & Maohua, X. (2010). The application of probabilistic neural network model in the green supply chain performance evaluation for pig industry. In International conference on e-business and e-government.
14. Kayaer K., Yildirim T. Medical diagnosis on pima indian diabetes using general regression neural networks. Artificial neural networks and neural information processing 2003.
15. Kiyan T., Yildirim T. Breast cancer diagnosis using statistical neural networks. Journal of Electrical & Electronics Engineering 2004, 4(2):1149-1153.
16. Lee E.W.M., Lim C.P., Yuen R.K.K., Lo S.M. A hybrid neural network model for noisy data regression. IEEE Transactions on Systems, Man, and Cybernetics 2004, 34(2):951-960.
17. Madsen K., Nielsen H.B., Tingleff O. Methods for non-linear least squares problems 2004, Informatics and Mathematical Modeling Technical University of Denmark.
18. Mangasarian O.L., Setiono R., Wolberg W.H. Pattern recognition via linear programming: theory and application to medical diagnosis. Large-scale numerical optimization 1990, 22-30. SIAM Publications.
19. Mangasarian O.L., Wolberg W.H. Cancer diagnosis via linear programming. SIAM News 1990, 23(5):1-18.
20. Mao K., Tan K., Set W. Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks 2000, 11(4):1009-1016.
21. Masters, T., & Land, W. (1997). A new training algorithm for the general regression neural network. In IEEE international conference on systems, man and cybernetics, computational cybernetics and simulation. Vol. 3 (pp. 1990-1994).
22. Montana D. A weighted probabilistic neural network. Advances in neural information processing systems 1992, 4:1110-1117.
23. Mosier P.D., Jurs P.C. QSAR/QSPR studies using probabilistic neural networks and generalized regression neural networks. Journal of Chemical Information and Computer Sciences 2002, 42:1460-1470.
24. Popescu, I., Kanatas, A., Constantinou, P., & Nafornita, I. (2002). Application of general regression neural networks for path loss prediction. In Proceedings of international workshop trends and recent achievements in information technology.
25. Ren, S., Yang, D., Ji, F., & Tian, X. (2010). Application of generalized regression neural network in prediction of cement properties. In 2010 International conference on computer design and applications.
26. Rutkowski L. Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Transactions on Neural Networks 2004, 15(4):811-827.
27. Specht D.F. Probabilistic neural networks. Neural Networks 1990, 3:109-118.
28. Specht D.F. A general regression neural network. IEEE Transactions on Neural Networks 1991, 2(6):568-576.
29. Tomandl D., Schober A. A modified general regression neural network with new efficient training algorithms as a robust 'black box'-tool for data analysis. Neural Networks 2001, 14:1023-1034.
30. Wang, Z., & Sheng, H. (2010). Rainfall prediction using generalized regression neural network: case study Zhengzhou. In 2010 International conference on computational and information sciences.
31. Wolberg W.H., Mangasarian O. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proceedings of the National Academy of Sciences 1990, 87:9193-9196.
32. Yildirim, T., & Cigizoglu, H. K. (2002). Comparison of generazlized regression neural network and MLP performances on hydrologic data forecasting. In International conference on neural information processing.
33. Yoo, P. D., Sikder, A. R., Zhou, B. B., & Zomaya, A. Y. (2007). Improved general regression network for protein domain boundary prediction. In Sixth international conference on bioinformatics.
34. Zhao, S., Zhang, J., Li, X., & Song, W. (2007). A generalized regression neural network based on fuzzy means clustering and its application in system identification. In International symposium on information technology convergence.
35. Zhu, C., & Hao, Z. (2009). Application of probabilistic neural network model in evaluation of water quality. In Internation conference on environmental science and information application technology.