Distance function learning for supervised similarity assessment

Studies in Computational Intelligence

Volumn 73, Issue , 2008, Pages 91-126

  Bagherjeiran, A ^a   Eick, C F ^a  

^a University of Houston   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 42449119790     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-73180-1_3     Document Type: Article

References (52)

1. Niloofar Arshadi and Igor Jurisica. Maintaining case-based reasoning systems: A machine learning approach. In Proc. 7th European Conf. on Adv. in Case-Based Reasoning, LNAI 3155, pages 17-31, Madrid, Spain, September 2004.
2. Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, and Ricardo Vilalta. Adaptive clustering: Obtaining better clusters using feedback and past experience. In Proc. 5th Int'l Conf. on Data Mining, pages 565-568, Houston, TX, USA, November 2005. IEEE Computer Society.
3. Maria-Florina Balcan and Avrim Blum. On a theory of learning with similarity functions. In Proc. 23rd Int'l Conf. on Machine Learning pages 73-80, 2006.
4. C.L. Blake and C.J. Merz. UCI repository of machine learning databases, 1998.
5. Leon Bobrowski and Magdalena Topczewska. Improving the k-NN classification with the Euclidean distance through linear data transformations. In Petra Perner, editor, Adv. in Data Mining, Applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications, LNAI 3275, pages 23-32. Springer Verlag, 2004.
6. Ingwer Borg and Patrick Groenen. Modern Multidimensional Scaling: Theory and Applications. Springer, New York, 1997.
7. Derek Bridge and Alex Ferguson. Learning weight intervals for classification. In Diarmuid O'Donoghue, editor, Proc. 12th Irish Conf. on Artif. Intell. & Cognitive Science, pages 95-104, 2001.
8. Chris Buckley and Gerard Salton. Optimization of relevance feedback weights. In Proc. 18th Int'l Conf. on Research and Development in Information Retrieval, pages 351-357, New York, NY, USA, 1995. ACM Press.
9. Stephane Canu and Yves Grandvalet. Adaptive scaling for feature selection in SVMs. In Adv. in Neural Information Processing Systems 15, pages 553-560. MIT Press, 2002.
10. T. Cover and P. Hart. Nearest neighbor pattern classi.cation. In IEEE Trans. on Information Theory, volume 13, pages 21-27, 1967.
11. Lorcan Coyle and Pádrain Cunningham. Improving recommendation ranking by learning personal feature weights. In Proc. 7th European Conf. on Adv. in Case-Based Reasoning, LNAI 3155, pages 560-572, Madrid, Spain, September 2004.
12. Ronan Cummins and Colm O'riordan. Evolving general term-weighting schemes for information retrieval: Tests on larger collections. Artificial Intelligence Review, 24(3-4):277-299, 2005.
13. Carlotta Domeniconi and D. Gunopulos. Adaptive nearest neighbor classification using support vector machines. In Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani, editors, Adv. in Neural Information Processing Systems 14, pages 665-672. MIT Press, 2001.
14. Carlotta Domeniconi, Jing Peng, and Dimitrios Gunopulos. An adaptive metric machine for pattern classification. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Adv. in Neural Information Processing Systems 13, pages 458-464. MIT Press, 2000.
15. Anastasios D. Doulamis and Nikolaos D. Doulamis. A recursive optimal relevance feedback scheme for content based image retrieval. In Proc. 2001 Int'l Conf. on Image Processing, volume 2, pages 741-744, 2001.
16. Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. John Wiley & Sons, New York, 2nd edition, 2001.
17. Christoph F. Eick, Alain Rouhana, Abraham Bagherjeiran, and Ricardo Vilalta. Using clustering to learn distance functions for supervised similarity assessment. Int'l Scientific Journal of Engineering Applications of Arti.cial Intelligence, 19(4):395-401, June 2006.
18. Christoph F. Eick and Nidal M. Zeidat. Using supervised clustering to enhance classifiers. In Proc. 15th Int'l Symp. on Methodologies for Intelligent Systems, pages 248-256, Saratoga Springs, NY, May 2005.
19. Amir Globerson and Sam Roweis. Metric learning by collapsing classes. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Adv. in Neural Information Processing Systems 18, pages 451-458. MIT Press, Cambridge, MA, 2005.
20. Jacob Goldberger, Sam Roweis, Geoffrey Hinton, and Ruslan Salakhutdinov. Neighbourhood components analysis. In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Adv. in Neural Information Processing Systems 17, pages 513-520. MIT Press, Cambridge, MA, 2004.
21. M. Halkidi, D. Gunopulos, N. Kumar, M. Vazirgiannis, and C. Domeniconi. A framework for semi-supervised learning based on subjective and objective clustering criteria. In Proc. 5th Int'l Conf. on Data Mining, pages 637-640, 2005.
22. Trevor Hastie and Robert Tibshirani. Discriminant adaptive nearest neighbor classification and regression. In David S. Touretzky, Michael C. Mozer, and Michael E. Hasselmo, editors, Adv. in Neural Information Processing Systems 8, pages 409-415. The MIT Press, 1996.
23. Aharon Bar Hillel, Tomer Hertz, Noam Shental, and Daphna Weinshall. Learning distance functions using equivalence relations. In Proc. 20th Int'l Conf. on Machine Learning, pages 11-18, 2003.
24. Joshua Zhexue Huang, Michael K. Ng, Honqiang Rong, and Zichen Li. Automated variable weighting in k-means type clustering. Trans. on Pattern Analysis and Machine Intelligence, 27(5):657-668, May 2005.
25. Jacek Jarmulak, Susan Craw, and Ray Rowe. Genetic algorithms to optimise CBR retrieval. In Proc. 5th European Workshop on Adv. in Case-Based Reasoning, pages 136-147, Tento, Italy, September 2000.
26. Jaz Kandola, John Shawe-Taylor, and Nello Cristianini. Learning semantic similarity. In Adv. in Neural Information Processing Systems 15. MIT Press, 2002.
27. Deok-Hwan Kim and Chin-Wan Chung. QCluster: Relevance feedback using adaptive clustering for content-based image retrieval. In Proc. ACM Int'l Conf. on Management of Data, pages 599-610, New York, NY, USA, 2003. ACM Press.
28. Kenji Kira and Larry A. Rendell. A practical approach to feature selection. In Proc. 9th Int'l Conf. on Machine Learning, pages 249-256, Aberdeen, Scotland, UK, 1992. Morgan Kaufmann.
29. Andreas Kohlmaier, Sascha Schmitt, and Ralph Bergmann. A similarity-based approach to attribute selection in user-adaptive sales dialogs. In Proc. 4th Int'l Conf. on Case-Based Reasoning, LNAI 2080, pages 306-320, Vancouver, BC, Canada, July 2001.
30. Igor Kononenko. Estimating attributes: Analysis and extensions of RELIEF. In Proc. 7th European Conf. on Machine Learning, pages 171-182, 1994.
31. David G. Lowe. Similarity metric learning for a variable-kernel classifier. Technical Report TR-93-43, 1993.
32. Héctor Núñez, Miquel Sánchez-Marrèe, and Ulises Cortées. Improving similarity assessment with entropy-based local weighting. In Proc. 5th Int'l Conf. on Case-Based Reasoning, LNAI 3689, pages 377-391, Trondheim, Norway, June 2003.
33. Atsuyuki Okabe, Barry Boots, and Kokichi Sugihara. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. John Wiley & Sons, Hoboken, NJ, 1992.
34. Rong Pan, Qiang Yang, and Lei Li. Case retrieval using nonlinear feature-space transformation. In Proc. 7th European Conf. on Adv. in Case-Based Reasoning, LNAI 3155, pages 361-374, Madrid, Spain, September 2004.
35. Dori Peleg and Ron Meir. A feature selection algorithm based on the global minimization of a generalization error bound. In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Adv. in Neural Information Processing Systems 17, pages 1065-1072. MIT Press, Cambridge, MA, 2004.
36. Petra Perner. Why case-based reasoning is attractive for image interpretation. In Proc. 4th Int'l Conf. on Case-Based Reasoning, LNAI 2080, pages 27-43, Vancouver, BC, Canada, July 2001.
37. J.J. Rocchio. Relevance feedback in information retrieval. In Gerard Salton, editor, The SMART Retrieval System-Experiments in Automatic Document Processing, chapter 14, pages 313-323. Prentice Hall, 1971.
38. Yong Rui, Thomas S. Huang, and Sharad Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. In Storage and Retrieval for Image and Video Databases, pages 25-36, San Jose, CA, January 1998.
39. Gerard Salton and Michael J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, NY, USA, 1983.
40. Sascha Schmitt, Philipp Dopichaj, and Patricia Domínguez-Marín. Entropy-based vs. similarity-influenced: Attribute selection methods for dialogs tested on different electronic commerce domains. In Proc. 6th European Conf. on Adv. in Case-Based Reasoning, LNAI 2416, pages 380-394, Aberdeen, Scotland, UK, September 2002.
41. Bernhard Schölkopf. The kernel trick for distances. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Adv. in Neural Information Processing Systems 13, pages 301-307. MIT Press, 2000.
42. Simon Chi Keung Shiu, Cai Hung Sun, Xi Zhao Wang, and Daniel So Yeung. Maintaining case-based reasoning systems using fuzzy decision trees. In Proc. 5th European Workshop on Adv. in Case-Based Reasoning, pages 285-296, Tento, Italy, September 2000.
43. Armin Stahl. Learning feature weights from case order feedback. In Proc. 4th Int'l Conf. on Case-Based Reasoning, LNAI 2080, pages 502-516, Vancouver, BC, Canada, July 2001.
44. Armin Stahl and Thomas Gabel. Using evolution programs to learn local similarity measures. In Proc. 5th Int'l Conf. on Case-Based Reasoning LNAI 3689, pages 537-551, Trondheim, Norway, June 2003.
45. Yijun Sun and Jian Li. Iterative RELIEF for feature weighting. In Proc. 23rd Int'l Conf. on Machine Learning, pages 913-920, 2006.
46. Eric C.C. Tsang, Simon C.K. Shiu, X.Z. Wang, and Martin Lam. Clustering and classification of cases using learned global feature weights. 2001.
47. Kilian Weinberger, John Blitzer, and Lawrence Saul. Distance metric learning for large margin nearest neighbor classification. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Adv. in Neural Information Processing Systems 18, pages 1473-1480. MIT Press, Cambridge, MA, 2005.
48. D. Wilson and D. Leake. Maintaining case-based reasoners: Dimensions and directions. Computational Intelligence, 17:196-212, 2001.
49. Nirmalie Wiratunga, Ivan Koychev, and Stewart Massie. Feature selection and generalisation for retrieval of textual cases. In Proc. 7th European Conf. on Adv. in Case-Based Reasoning, LNAI 3155, pages 806-820, Madrid, Spain, September 2004.
50. Liu Yang, Rong Jin, Rahul Sukthankar, and Yi Liu. An efficient algorithm for local distance metric learning. In Proc. 21st Nat'l Conference on Artificial Intelligence, 2006.
51. Lei Yu and Huan Liu. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proc. 20th Int'l Conf. on Machine Learning, pages 856-863, 2003.
52. Zhihua Zhang. Learning metrics via discriminant kernels and multidimensional scaling: Toward expected euclidean representation. In Proc. 20th Int'l Conf. on Machine Learning, pages 872-879, 2003.