An adaptively trained kernel-based nonlinear representor for handwritten digit classification

Journal of Electronics (China) - Tập 23 Số 3 - Trang 379-383 - 2006
Benyong Liu1, Jing Zhang2
1School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China
2School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

M. A. Kraaijveld. A Parzen classifier with an improved robustness against deviations between training and test data. Pattern Recognition Letters, 17 (1996) 7, 679–689.

C. Cortes, V. Vapnik. Support-vector networks. Machine Learning, 20(1995) 3, 273–297.

J. A. K. Suykens, J. Vandewalle. Least squares support vector machine classifier. Neural Processing Letters, 9(1999) 3, 293–300.

B. Y. Liu. A kernel-based nonlinear discriminator with closed-form solution. Proc. IEEE Int. Conf. Neural Network and Signal Processing, Nanjing, China, 2003, 41–44.

J. Zhang, B. Y. Liu, H. Tan. A kernel-based nonlinear representor with application to eigenface classification. J. Electronic Science and Technology of China, 2(2004) 2, 19–22.

B. Y. Liu. Adaptive training of a kernel-based nonlinear discriminator. Pattern Recognition (accepted).

T. Poggio, F. Girosi. Networks for approximation and learning. Proc. IEEE, 78(1990) 9, 1481–1479.

H. Ogawa. Neural network learning, generalization and over-learning. Proc. Int. Conf. Intelligent Information Processing & System (Supplemental volume), Beijing, China, 1992, 1–6.

B. Y. Liu, H. Ogawa. An equivalent form of S-L projection learning. J. Electronic Science and Technology of China, 1(2003) 1, 6–11.

S. Vijayakumar, H. Ogawa. RKHS-based functional analysis for exact incremental learning. Neurocomputing, Special Issue on Theoretical Analysis of Real Valued Function Classes, 29(1999)1–3, 85–113.

A. Albert. Regression and the Moor-Penrose Pseudoinverse. New York, Academic Press, 1972, 26–28.

R. Schatten. Norm Ideals of Completely Continuous Operators. Berlin, Springer, 1960, 7–10.

L. M. Fu, H. H. Hsu, J. C. Principe. Incremental backpropagation learning network. IEEE Trans. on Neural Networks, 7(1996) 3, 757–761.

D. C. Park, A. E. Sharkawi, R. J. Marks II. An adaptively trained neural network. IEEE Trans. on Neural Networks, 2(1991) 3, 334–345.

S. Vijayakumar, H. Ogawa. A functional analytic approach to incremental learning in optimally generalizing neural networks. Proc. IEEE Int. Conf. Neural Networks, Perth, Western Australia, 1995, 777–782.

B. Y. Liu, J. Zhang. Incremental POP learning. J. Electronic Science and Technology of China, 2(2004) 4, 29–36.

A. Albert. Conditions for positive and nonnegative definiteness in term of pseudoinverses. SIAM J. Appl. Math., 17(1969) 2, 434–440.

A. K. Jain. R. P. W. Duin, J. C. Mao. Statistical pattern recognition: A review. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(2000) 1, 4–37.