Abstract
For learning purposes, representations of real world objects can be built by using the concept of dissimilarity (distance). In such a case, an object is characterized in a relative way, i.e. by its dissimilarities to a set of the selected prototypes. Such dissimilarity representations are found to be more practical for some pattern recognition problems.
When experts cannot decide for a single dissimilarity measure, a number of them may be studied in parallel. We investigate two possibilities of combining either dissimilarity representations themselves or classifiers built on each of them separately. Our experiments conducted on a handwritten digit set demonstrate that when the dissimilarity representations are of different nature, a much better performance can be obtained by their combination than on individual representations.
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References
R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, Inc., 1973.
R.P.W. Duin. Classifiers for dissimilarity-based pattern recognition. In 15th Int. Conf. on Pattern Recognition, volume 2, pages 1–7, Barcelona (Spain), 2000.
R.P.W. Duin and D.M.J. Tax. Classifier conditional posterior probabilities. In Advances in Pattern Recognition, Lecture Notes in Computer Science, volume 1451, pages 611–619, Sydney, 1998. Proc. Joint IAPR Int. Workshops SSPR and SPR.
K. Fukunaga. Introduction to Statistical Pattern Recognition. Acad. Press, 1990.
Duin, R.P.W. Kittler, J., Hatef M. and Matas, J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226–239, 1998.
Dubuisson M. P. and Jain A. K. Modified hausdorff distance for object matching. In 12th Int. Conf. on Pattern Recognition, volume 1, pages 566–568, 1994.
E. Pekalska and R.P.W. Duin. Classifiers for dissimilarity-based pattern recognition. In 15th ICPR, volume 2, pages 12–16, Barcelona, 2000.
E. Pekalska and R.P.W. Duin. Automatic pattern recognition by similarity representations. Electronic Letters, 37(3):159–160, 2001.
Duin, R.P.W. Tax, D.M.J. and Kittler, J. Combining multiple classifiers by averaging or by multiplying? Pattern Recognition, 33(9):1475–1485, 2000.
C.L. Wilson and M.D. Garris. Handprinted character database 3. Technical report, National Institute of Standards and Technology, February 1992.
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© 2001 Springer-Verlag Berlin Heidelberg
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Pękalska, E., Duin, R.P.W. (2001). On Combining Dissimilarity Representations. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_36
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DOI: https://doi.org/10.1007/3-540-48219-9_36
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