Learning similarity functions from qualitative feedback

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

Volumn 5239 LNAI, Issue , 2008, Pages 120-134

  Cheng, Weiwei ^a   Hüllermeier, Eyke ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

CASE-BASED REASONING SYSTEMS; EUROPEAN; LEARNING SIMILARITY; LOCAL SIMILARITY; MACHINE-LEARNING; QUALITATIVE FEEDBACK; RANKING PERFORMANCE; SIMILARITY ASSESSMENT;

CASE BASED REASONING;

EID: 52149107143     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85502-6_8     Document Type: Conference Paper

References (26)

1. Richter, M.M.: Foundations of similarity and utility. In: The 20th International FLAIRS Conference, Key West, Florida (2007)
2. Stahl, A.: Learning similarity measures: A formal view based on a generalized CBR model. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 507-521. Springer, Heidelberg (2005)
3. Joachims, T.: Optimizing search engines using clickthrough data. In: KDD 2002, Proc. of the ACM Conference on Knowledge Discovery and Data Mining (2002)
4. Cunningham, P.: A taxonomy of similarity mechanisms for case-based reasoning. Technical Report UCD-CSI-2008-01, University College Dublin (2008)
5. Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
6. Khardon, R., Wachman, G.: Noise tolerant variants of the perceptron algorithm. The Journal of Machine Learning Research 8, 227-248 (2007)
7. Herbrich, R., Graepel, T., Campbell, C.: Bayes point machines. Journal of Machine Learning Research 1, 245-279 (2001)
8. Seung, H., Opper, M., Sompolinsky, H.: Query by committee. In: Computational Learning Theory, pp. 287-294 (1992)
9. Kendall, M.: Rank correlation methods. Charles Griffin, London (1955)
10. O'Leary, J.: World university rankings editorial - global vision ensures healthy competition. The Times Higher Education Supplement (2006)
11. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
12. Pavlidis, P., Weston, J., Cai, J., Grundy, W.: Gene functional classification from heterogeneous data. Journal of Comput. Biology 9, 401-411 (2002)
13. Cohen, W., Schapire, R., Singer, Y.: Learning to order things. Journal of Artificial Intelligence Research 10 (1999)
14. Stahl, A.: Learning feature weights from case order feedback. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 502-516. Springer, Heidelberg (2001)
15. Stahl, A., Schmitt, S.: Optimizing retrieval in CBR by introducing solution similarity. In: Proc. Int. Conf. on Art. Intell., ICAI, Las Vegas, USA (2002)
16. Stahl, A., Gabel, T.: Optimizing similarity assessment in case-based reasoning. In: Proc. 21th National Conf. on Artificial Intelligence. AAAI, Menlo Park (2006)
17. Stahl, A., Gabel, T.: Using evolution programs to learn local similarity measures. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 537-551. Springer, Heidelberg (2003)
18. Bonzano, A., Cunningham, P., Smyth, B.: Using introspective learning to improve retrieval in CBR: A case study in air traffic control. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266, pp. 291-302. Springer, Heidelberg (1997)
19. Kononenko, I.: Estimating attributes: Analysis and extensions of RELIEF. In: European Conference on Machine Learning, pp. 171-182 (1994)
20. Ricci, F., Avesani, P.: Learning a local similarity metric for case-based reasoning. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 301-312. Springer, Heidelberg (1995)
21. Wilke, W., Bergmann, R.: Considering decision cost during learning of feature weights. In: European Workshop on CBR, pp. 460-472 (1996)
22. Branting, K.: Acquiring customer preferences from return-set selections. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 59-73. Springer, Heidelberg (2001)
23. Wettschereck, D., Aha, D.: Weighting features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347-358. Springer, Heidelberg (1995)
24. Wu, Y., Ianakiev, K., Govindaraju, V.: Improvements in k-nearest neighbor classification. In: Singh, S., Murshed, N., Kropatsch, W.G. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 222-229. Springer, Heidelberg (2001)
25. Toussaint, G.: Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining. Int. J. Comput. Geometry Appl. 15(2), 101-150 (2005)
26. Torra, V., Narukawa, Y.: Modeling Decisions: Information Fusion and Aggregation Operators. Springer, Heidelberg (2007)