A conformal classifier for dissimilarity data

IFIP Advances in Information and Communication Technology

Volumn 382 AICT, Issue PART 2, 2012, Pages 234-243

  Schleif, Frank Michael ^a   Zhu, Xibin ^a   Hammer, Barbara ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

BIOLOGICAL SEQUENCES; CLASSIFICATION ALGORITHM; COMPLEX TRANSFORMATIONS; CONFIDENCE MEASURE; CRISP CLASSIFICATION; DISSIMILARITY DATUM; MODEL COMPLEXITY; STANDARD REQUIREMENTS;

CLASSIFICATION (OF INFORMATION);

EID: 84870875312     PISSN: 18684238     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-33412-2_24     Document Type: Conference Paper

References (16)

1. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O'Donovan, C., Phan, I., Pilbout, S., Schneider, M.: The swissprot protein knowledgebase and its supplement trembl in 2003. Nucl. Ac. Res. 31, 365-370
2. Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Cazzanti, L.: Similarity-based classification: Concepts and algorithms. J. of Mach. Learn. Res. 10, 747-776 (2009)
3. Duin, R.P.W.: PRTools (March 2012), http://www.prtools.org
4. Duin, R.P.W., Loog, M., Pȩkalska, E.z., Tax, D.M.J.: Feature-Based Dissimilarity Space Classification. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 46-55. Springer, Heidelberg (2010)
5. Gasteiger, E.: Expasy: the proteomics server for in-depth protein knowledge and analysis. Nuc. Ac. Res. 31(3784-3788) (2003)
6. Gusfield, D.: Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press (1997)
7. Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity data sets. Neural Computation 22(9), 2229-2284 (2010)
8. Hammer, B., Schleif, F.-M., Zhu, X.: Relational Extensions of Learning Vector Quantization. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part II. LNCS, vol. 7063, pp. 481-489. Springer, Heidelberg (2011)
9. Lozano, M., Pekalska, E., Duin, R.P.W.: Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces. Pattern Recognition 39(10), 1827-1838 (2006)
10. Pekalska, E., Duin, R.P.W.: The dissimilarity representation for pattern recognition. World Scientific (2005)
11. Pekalska, E., Duin, R.P.W., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189-208 (2006)
12. Proedrou, K., Nouretdinov, I., Vovk, V., Gammerman, A.: Transductive Confidence Machines for Pattern Recognition. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 381-390. Springer, Heidelberg (2002)
13. Sato, A., Yamada, K.: Generalized learning vector quantization. In: NIPS, pp. 423-429 (1995)
14. Schleif, F.-M., Villmann, T., Hammer, B., Schneider, P.: Efficient kernelized prototype based classification. Int. J. Neural Syst. 21(6), 443-457 (2011)
15. Shafer, G., Vovk, V.: A tutorial on conformal prediction. JMLR 9, 371-421 (2008)
16. Vapnik, V.: The nature of statistical learning theory. Stat. f. Eng. & Inf. Sc. Springer (2000)