Robustness through prior knowledge: Using explanation-based learning to distinguish handwritten Chinese characters

International Journal on Document Analysis and Recognition

Volumn 10, Issue 3-4, 2007, Pages 175-186

  Sun, Qiang ^a   Wang, Li Lun ^a   Lim, Shiau Hong ^a   DeJong, Gerald ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Characterr ecognition; Domain knowledge; Explanation based learning; Machine learning; Support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; SUPPORT VECTOR MACHINES; TRANSLATION (LANGUAGES);

DOMAIN KNOWLEDGE; EXPLANATION-BASED LEARNING; HANDWRITTEN CHARACTERS;

CHARACTER RECOGNITION;

EID: 37249066672     PISSN: 14332833     EISSN: 14332825     Source Type: Journal    
DOI: 10.1007/s10032-007-0053-1     Document Type: Conference Paper

References (25)

1. Bartlett, P., Shawe-Taylor, J.: Generalization performance of support vector machines and other pattern classifiers. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge (1998)
2. ChAng, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm
3. Cristianini, N., Shawe-Taylor, J., Elisseeff, A.: On kernel-target alignment. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems (NIPS), vol. 14, pp. 367-373. MIT Press, Cambridge (2002)
4. DeCoste D. and Schölkopf B. (2002). Training invariant support vector machines. Mach. Learn. 46: 161-190
5. Forsyth D.A. and Ponce J. (2002). Computer Vision - A Modern Approach. Prentice-Hall, Englewood Cliffs
6. Fung G.M., Mangasarian O.L. and Shavlik J.W. (2003). Knowledge-based nonlinear kernel classifiers. In: Schölkopf, B. and Warmuth, M.K. (eds) Learning Theory and Kernel Machines: 16th Annual Conference on Computational Learning Theory (COLT), Lecture Notes in Computer Science, vol. 2777, pp 102-113. Springer, Heidelberg
7. Fung G.M., Mangasarian O.L. and Shavlik J.W. (2003). Knowledge-based support vector machine classiers. In: Becker, S.T.S. and Obermayer, K. (eds) Advances in Neural Information Processing Systems (NIPS), vol. 15, pp 521-528. MIT Press, Cambridge
8. Kandola, J., Shawe-Taylor, J., Cristianini, N.: Optimizing kernel alignment over combinations of kernels. Technical Report NC-TR-02-121, NeuroCOLT (2002)
9. LeCun Y., Bottou L., Bengio Y. and Haffner P. (1998). Gradient-based learning applied to document recognition. In: Proceedings of the IEEE 86(11): 2278-2324
10. LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of learning algorithms for handwritten digit recognition. In: Fogelman, F., Gallinari, P. (eds.) International Conference on Artificial Neural Networks, pp. 53-60 (1995)
11. Lim, S.H., Wang, L.L., DeJong, G.: Explanation-based feature construction. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 931-936 (2007)
12. Mangasarian O.L., Shavlik J.W. and Wild E.W. (2004). Knowledge-based kernel approximation. J. Mach. Learn. Res. 5: 1127-1141
13. Mitchell T. (1997). Machine Learning. McGraw-Hill, New York
14. Ong, C.S., Smola, A.: Machine learning with hyperkernels. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML) (2003)
15. Saito T., Yamada H. and Yamamoto K. (1985). On the data base ETL 9 of handprinted characters in JIS Chinese characters and its analysis. IEICE Trans. J68-D(4): 757-764
16. Schölkopf, B., Burges, C., Vapnik, V.: Incorporating invariances in support vector learning machines. In: Von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds.) Artificial Neural Networks - ICANN '96, vol. 1112, pp. 47-52 (1996)
17. Schölkopf B., Simard P., Vapnik V. and Smola A.J. (1998). Prior knowledge in support vector kernels. In: Jordan, M.I., Kearns, M.J., and Solla, S.A. (eds) Advances in Neural Information Processing Systems (NIPS), vol. 10, pp 640-646. MIT Press, Cambridge
18. Shawe-Taylor J., Bartlett P.L., Williamson R.C. and Anthony M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE Trans. Inform. Theor. 44(5): 1926-1940
19. Shawe-Taylor J. and Cristianini N. (2002). On the generalisation of soft margin algorithms. IEEE Trans. Inform. Theor. 48(10): 2721-2735
20. Shi D., Damper R.I. and Gunn S.R. (2003). Off-line handwritten chinese character recognition by radical decomposition. ACM Trans. Asian Lang. Process. 2(1): 27-48
21. Simard P.Y., Le Cun Y.A., Denker J.S. and Victorri B. (1998). Transformation invariance in pattern recognition - tangent distance and tangent propagation. In: Orr, G.B. and Müller, K.R. (eds) Neural Networks: Tricks of the Trade, Chap. 12., pp 239-274. Springer, Heidelberg
22. Simard, P.Y., Steinkraus, D., Platt, J.: Best practice for convolutional neural networks applied to visual document analysis. In: International Conference on Document Analysis and Recogntion (ICDAR), pp. 958-962. IEEE Comput. Soc., Los Alamitos (2003)
23. Sun, Q., DeJong, G.: Axplanation-augmented SVM: an approach to incorporating domain knowledge into SVM learning. In: De Raedt, L., Wrobel, S. (eds) Proceedings of the 22nd International Machine Learning Conference. ACM Press, New York (2005)
24. Teow, L.N., Loe, K.F.: Handwritten digit recognition with a novel vision model that extracts linearly separable features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 76-81 (2000)
25. Vapnik V.N. (1998). Statistical Learning Theory. Wiley, New York