Theory and algorithm for learning with dissimilarity functions

Neural Computation

Volumn 21, Issue 5, 2009, Pages 1459-1484

  Wang, Liwei ^a   Sugiyama, Masashi ^b   Yang, Cheng ^a   Hatano, Kohei ^c   Feng, Jufu ^a  

^a Peking University   (China)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^c KYUSHU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; HUMAN; INFORMATION RETRIEVAL; LEARNING; METHODOLOGY; PERCEPTIVE DISCRIMINATION; PHYSIOLOGY; PSYCHOLOGICAL THEORY;

ALGORITHMS; COMPUTER SIMULATION; DISCRIMINATION (PSYCHOLOGY); HUMANS; INFORMATION STORAGE AND RETRIEVAL; LEARNING; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PSYCHOLOGICAL THEORY;

EID: 70349263820     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2008.08-06-805     Document Type: Letter

References (32)

1. Asuncion, A., & Newman, D. J. (2007). UCI machine learning repository. 2007. Available online at http://archive.ics.uci.edu/ml.
2. Balcan, M.-F., & Blum, A. (2006). On a theory of learning with similarity functions. In W. W. Cohen & A. Moore (Eds.), Proceedings of the International Conference on Machine Learning. New York: ACM Press.
3. Balcan, M.-F., Blum, A., & Srebro, N. (2008a). Improved guarantees for learning via similarity functions. In Proceedings of the 21st Annual Conference on Learning Theory.Madison,WI:Omnipress.
4. Balcan, M.-F., Blum, A., & Srebro, N. (2008b). A theory of learning with similarity functions. Machine Learning, 72, 89-112.
5. Balcan, M.-F., Blum, A., & Vempala, S. (2004). On kernels, margins, and low- dimensional mappings. In Proceedings of the International Workshop on Algorithmic Learning Theory.NewYork:Springer.
6. Balcan, M.-F., Blum, A., & Vempala, S. (2006). Kernels as features: On kernels, margins, and low-dimensional mappings. Machine Learning, 65, 79-94.
7. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.
8. Chang, C. C., & Lin, C. J. (2001). A library for support vector machines. Available online at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
9. Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the International Conference on Machine Learning. San Francisco: Morgan Kaufmann.
10. Fukunaga, K. (1990). Introduction to statistical pattern recognition (2nd ed.). San Diego, CA: Academic Press.
11. Gärtner, T. (2003). A survey of kernels for structured data. SIGKDD Explorations, 5(1), S268-S275.
12. Goldfarb, L. (1985). A new approach to pattern recognition. In L. N. Kannal & A. Rosenfeld (eds.), Progress in pattern recognition (vol. 2, pp. 241-402). Dordrecht: Elsevier.
13. Graepel, T., Herbrich, R., Bollmann-Sdorra, P., & Obermayer, K. (1999). Classification on pairwise proximity data. In S. A. Solla, T. K. Leen, & K.-R. Muller (Eds.), Advances in neural information processing systems, 12. Cambridge, MA: MIT Press.
14. Hagio, K. (2006). Design and evaluation of string similarity measure based kernels Unpublished bachelor's thesis, Kyushu University (in Japanese).
15. Hart, P., & Cover, T. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21-27.
16. Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850-863.
17. Jacobs, D. W., Weinshall, D., & Gdalyahu, Y. (2000). Classification with nonmetric distances: Image retrieval and class representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(6), 583-600.
18. Jain, A. K., & Zongker, D. E. (1997). Representation and recognition of handwritten digits using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(12), 1386-1391.
19. Li, J., Chen, G., & Chi, Z. (2002). A fuzzy image metric with application to fractal coding. IEEE Transactions on Image Processing, 11(6), 636-643.
20. Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2003). Handbook of fingerprint recognition.NewYork:Springer.
21. Pekalska, E., & Duin, R. P. W. (2002). Dissimilarity representations allow for building good classifiers. Pattern Recognition Letters, 23(8), 943-956.
22. Saigo, H., Vert, J.-P., Ueda, N., & Akutsu, T. (2004). Protein homology detection using string alignment kernels. Bioinformatics, 11, 1682-1689.
23. Schapire, R., Freund, Y., Bartlett, P., & Lee, W. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26, 1651-1686.
24. Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidencerated predictions. Machine Learning, 37, 297-336.
25. Shimodaira, H. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2), 227-244.
26. Simard, P., LeCun, Y., & Denker, J. (1993). Efficient pattern recognition using a new transformation distance. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems, 5. San Francisco: Morgan Kaufmann.
27. Srebro, N. (2007). How good is a kernel when used as a similarity measure? In Proceedings of the 20th Annual Conference on Learning Theory.NewYork:Springer.
28. Sugiyama, M., Krauledat, M., & Müller, K.-R. (2007). Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research, 8, 985-1005.
29. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
30. Wang, L., Sugiyama, M., Yang, C., Zhou, Z., & Feng, J. (2008). On the margin explanation of boosting algorithms. In Proceedings of the 21st Annual Conference on Learning Theory.Madison,UI:Omnipress.
31. Wang, L., Zhang, Y., & Feng, J. (2005). On the Euclidean distance of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1334-1339.
32. Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35, 399-458.