On not making dissimilarities euclidean

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3138, Issue , 2004, Pages 1145-1154

  Pçkalska, Elzbieta ^a   Duin, Robert P W ^a   Günter, Simon ^b   Bunke, Horst ^b  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); GEOMETRY; PATTERN RECOGNITION;

DISCRIMINATING POWER; DISSIMILARITY DATUM; DISSIMILARITY MEASURES; DISSIMILARITY REPRESENTATIONS; K-NEAREST-NEIGHBOR RULE; PSEUDO-EUCLIDEAN SPACE; STRUCTURAL DESCRIPTIONS; TRADITIONAL LEARNING;

NEAREST NEIGHBOR SEARCH;

EID: 33749612653     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (15)

1. C.L. Blake and C.J. Merz. UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences, 1998.
2. H. Bunke, S. Günter, and X. Jiang. Towards bridging the gap between statistical and structural pattern recognition: Two new concepts in graph matching. In Conf. on Advances in Pattern Recognition; LNCS 2013, pages 1-11, 2001.
3. C.K. Chow. On optimum recognition error and reject tradeoff. IEEE Trans, on Information Theory, IT-16(l):41-46, 1970.
4. P. Courrieu. Straight monotonie embedding of data sets in Euclidean spaces. Neural Networks, 15:1185-1196, 2002.
5. L. Goldfarb. A new approach to pattern recognition. In Progress in Pattern Recognition, volume 2, pages 241-402. Elsevier Science Publishers B.V., 1985.
6. J.C. Cower. Metric and Euclidean Properties of Dissimilarity Coefficients. Journal of Classification, 3:5-48, 1986.
7. D.W. Jacobs, D. Weinshall, and Y. Gdalyahu. Classification with Non-Metric Distances: Image Retrieval and Class Representation. IEEE Trans, on PAMI, 22(6):583-600, 2000.
8. A.K. Jain and D. Zongker. Representation and recognition of handwritten digits using deformable templates. IEEE Trans, on PAMI, 19(12):1386-1391, 1997.
9. F. Moreno-Seco, L. Mica, and J. Oncina. A modification of the LAESA algorithm for approximated k-nn clasification. Pattern Recogn. Letters, 24(1-3):47-53, 2003.
10. 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.
11. E. Pçkalska. working title: Dissimilarity-based pattern recognition. PhD thesis, Delft University of Technology, The Netherlands, to appear, 2004.
12. E. Pçkalska and R.P.W. Duin. Dissimilarity representations allow for building good classifiers. Pattern Recogn. Letters, 23(8):943-956, 2002.
13. E. Pekalska, P. Paclík, and R.P.W. Duin. A Generalized Kernel Approach to Dissimilarity Based Classification. J. of Mach. Learn. Research, 2:175-211, 2001.
14. V. Roth, J. Laub, J.M. Buhmann, and K.-R. Müller. Going metric: Denoising pairwise data. In Advances in NIPS 15, pages 841-856. MIT Press, 2003.
15. C.L. Wilson and M.D. Garris. Handprinted character database 3. Technical report, National Institute of Standards and Technology, February 1992.