Explanation-based feature construction

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 931-936

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

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

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION INFORMATIONS; CLASSIFICATION TASKS; DISCRIMINATIVE FEATURES; FEATURE CONSTRUCTION; HANDWRITTEN CHINESE CHARACTER; HIGH-LEVEL FEATURES; NONLINEAR COMBINATION; SUPERVISED MACHINE LEARNING;

ARTIFICIAL INTELLIGENCE; NEONATAL MONITORING;

CLASSIFICATION (OF INFORMATION);

EID: 37249093767     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Almuallim, H., Dietterich, T.G. Learning with many irrelevant features, in Ninth National Conference on AI, MIT Press, pp.547-552, 1991.
2. Bartlett, P.L., Mendelson, S. Rademacher and Gaussian complexities: Risk bounds and structural results. J. of Machine Learning Research, 3:463-482, 2002.
3. Battiti, R. Using mutual information for selecting features in supervised neural net learning. Neural Networks, 5(4):537-550, 1994.
4. Blum, A., Langley, P. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1-2):245-271, 1997. (Pubitemid 127401106)
5. Ding, X., Chinese Character Recognition, in Handbook of Pattern Recognition and Computer Vision, 3rd ed., pp. 241-257,World Scientific, 2005.
6. Felzenszwalb, P., Huttenlocher, D. Efficient Matching of Pictorial Structures, CVPR 2000, pp. 66-73, 2000.
7. Feder, M., Merhav, N. Relations between entropy and error probability. IEEE Trans. on Information Theory, 40:259-266, 1994.
8. Forsyth, D.A., Ponce, J. Computer Vision: A Modern Approach, Prentic Hall, 2002.
9. Gabrilovich, E., Markovitch, S. Feature Generation for Text Categorization Using World Knowledge, IJCAI 2005, pp. 1048-1053, 2005.
10. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. Feature Extraction, oundations and Applications. Springer 2006.
11. Kimura, F., Wakabayashi, T., Tsuruoka, S., Miyake, Y. Improvement of Handwritten Japanese Character Recognition usingWeighted Direction Code Histogram, Pattern Recognition, Vol. 30, No. 8, pp. 1329-1337, 1997. (Pubitemid 127417901)
12. Kira, K, Rendell, L.A. A practical approach to feature selection, in Proc. of the 9th ICML, 1992.
13. Kohavi, R., John, G. Wrappers for feature selection. Artificial Intelligence, 97(1-2):273-324, December 1997.
14. Lu, S.W., Ren, Y., Suen, C.Y. Hierarchical attributed graph representation and recognition of handwritten chinese characters. Pattern Recognition, Vol. 24, No. 7, pp. 617-632, 1991.
15. Markovitch, S., Rosenstein, D. Feature Generation Using General Constructor Functions, Machine Learning, Vol. 49, No. 1, pp. 59-98, 2002. (Pubitemid 34325695)
16. Suen, C.Y., Mori, S., Kim, S.H., Leung, C.H. Analysis and Recognition of Asian Scripts - the State of the Art, ICDAR '03, pp. 866-878, 2003.
17. Sun, Q., DeJong, G. Feature Kernel Functions: Improving SVMs Using High-Level Knowledge. CVPR (2) 2005: 177-183, 2005.
18. Tishby, N., Pereira, F., Bialek, W. The information bottleneck method. In Proc. of the 37th Ann. Allerton Conf. on Communication, Control and Computing, pp. 368-377, 1999.
19. Torkkola, K. Feature extraction by nonparametric mutual information maximization. J. of Machine Learning Research, 3:1415-1438, 2003.