Taxonomy of classifiers based on dissimilarity features

Lecture Notes in Computer Science

Volumn 3686, Issue PART I, 2005, Pages 136-145

  Raudys, Sarunas ^a  

^a VILNIUS UNIVERSITY   (Lithuania)

Author keywords

Classification; Dissimilarity; Neural networks; Single layer perceptron; Training

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; FEATURE EXTRACTION; FUNCTION EVALUATION; MATHEMATICAL MODELS; NEURAL NETWORKS;

DISSIMILARITY; KERNEL FUNCTIONS; SINGLE LAYER PERCEPTRON; TRAINING;

CLASSIFICATION (OF INFORMATION);

EID: 27244457970     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11551188_15     Document Type: Conference Paper

References (25)

1. Arkedev A.G., Braverman E.M. Computers and Pattern Recognition: Thompson, Washington, DC, 1966.
2. Duin R.P.W. Compactness and complexity of pattern recognition problems. In: Perneel C., editor. Proceedings of the Int. Symposium on Pattern Recognition. In Memoriam Pierre Devijver, Brussels, B, February 12. Brussels: Royal Military Academy; p. 124-128, 1999.
3. Duda R.O., Hart .P.E. and Stork D.G. Pattern Classification and Scene Analysis. 2nd edition. John Wiley, New York, 2000.
4. Fix E, Hodges JLJr. Discriminatory analysis, nonparametric discrimination: consistency properties. Report No. 4, Project 21-49-004. USAF School of Aviation Medicine, Randolph Field, TX, 1951.
5. Attneave F. Dimensions of similarity, Am. J. Psychology, 63: 516-556, 1950.
6. Parzen E. On estimation of probability function and mode. Annals of Mathematical Statistics, 33:1065-76, 1962.
7. Wolverton C.T. and Wagner T.J. Asymptotically optimal discriminant functions for pattern classification. IEEE Transactions on Information Theory, IT-15:258-65, 1969.
8. Boser B., Guyon, I. and Vapnik V. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh: ACM, pp. 144-152, 1992.
9. Scholkopf B., Burges CJC. and Smola A.J. Advances in Kernel Methods: Support vector learning. MIT Press, Cambridge, MA, 1999.
10. Cristianini N. and Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge Univ. Press, Cambridge, UK, 2000.
11. Aha, D.W., Kibler, D., Albert, M.K. Instance-based learning algorithms. Machine Learning, 6, 37-66, 1991.
12. Althoff, K.-D. Case-based reasoning. In: S. K. Chang (Ed.), Handbook on Software Engineering and Knowledge Engineering. Vol. 1 (Fundamentals), World Scientific, 549-588, 2001.
13. Perner P. Are case-based reasoning and dissimilarity-based pattern recognition two sides of the same coin? Lecture Notes in Artificial Intelligence. Springer-Verlag, 2123: 35-51, 2001.
14. Schuermann J. Pattern Classification: A unified view of statistical and neural approaches, John Wiley, New York, 1996.
15. Raudys S. Statistical and Neural Classifiers: An integrated approach to design. Springer, London, 2001, 2001.
16. Duin R.P.W., Pekalska E., and De Ridder D. Relational discriminant analysis. Pattern Recognition Letters, 20 (11-13): 1175-1181, 1999.
17. Paclik P. and Duin R.P.W. Dissimilarity-based classification of spectra: computational issues. Real-Time Imaging, 9: 237-244, 2003.
18. Mottl, V. Seredin, O. Dvoenko, S. Kulikowski, C. Muchnik, I. Featureless pattern recognition in an imaginary Hilbert space. Proc. 16th International Conference on Pattern Recognition. Vol. 2: 88- 91, 2002.
19. Santini S. and Jain R. Similarity measures. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9): 871-883, 1999.
20. Pekalska E. and Duin R.P.W. Dissimilarity representations allow for building good classifiers. Pattern Recognition Letters, 23(8): 943-56, 2002.
21. Pekalska E. Ph. D. Thesis. Delft University of Technology, 2005.
22. Pekalska E., Paclik P. and Duin R.P.W. A generalized kernel approach to dissimilarity based classification. Journal of Machine Learning Research, 2: 175-211, 2002.
23. Raudys S. Evolution and generalization of a single neurone. I. SLP as seven statistical classifiers. Neural Networks, 11: 283-96, 1998.
24. Raudys S. How good are support vector machines? Neural Networks, 13, 9-11, 2000.
25. Raudys S. and Iwamura M. Structures of covariance matrix in handwritten character recognition. Lecture Notes in Computer Science, Springer-Verlag. LNCS, 3138: 725-733, 2004.