On two-pattern classification and feature selection using neural networks

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 1994, Pages II617-II620

  Lee, Luan Ling ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; LEARNING ALGORITHMS; SIGNAL PROCESSING;

FALSE ACCEPTANCE ERRORS; FALSE REJECTION; MISCLASSIFICATIONS; NEURAL NETWORK (NN); NN CLASSIFIERS; PERCEPTRON LEARNING; SIGNATURE VERIFICATION; SINGLE NEURON;

CLASSIFICATION (OF INFORMATION);

EID: 50649098553     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.1994.389580     Document Type: Conference Paper

References (9)

1. K. Fukunaga and W. L. G. Koontz, "Application of Karhunen-Loeve expansion to feature selection and ordering," IEEE Trans. Computer, vol. C-19, No. 4, pp. 311-318, April, 1970.
2. S. I. Gallant, "Perceptron-based Learning Algorithms," IEEE Tmns. Neural Networks, vol. l, No. 2, pp. 179-191, June 1990.
3. L. L. Lee and T. Berger, "On twc-pattern classification using neural networks," Int. Conf. on Signai Processing ICSP'93, Beijing-China, Oct.25-29, 1993.
4. L. L. Lee, "On-line systems for human signature verification," Ph.D. thesis, Cornell University, School of Electrical Engineering, January 1992.
5. L. L. Lee and T. Berger, "Optimization of a signature verification system using neural networks," Int. Conf. on Artificial Neural Networks - ICANN, Amsterdam- Netherlands, Sept. 13-16, 1993.
6. R. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
7. C. Lee and D. Landgrebe, "Feature Extraction Based on Decision Boundary," IEEE Trans. on PAMI vol. 15, No. 4, pp. 388-400, April 1993.
8. R.G. Gallager, Information Theory and Reliable Communication. New York: Wiley.
9. L.L. Lee, "Two Pattern Classification and Feature Extraction Based on Minimum Error Decision Boundary Using Neural Networks" 1994 IEEE Int. Sym on Information Theory. To appear.