On learning asymmetric dissimilarity measures

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 697-700

  Kummamuru, Krishna ^a   Krishnapuram, Raghu ^a   Agrawal, Rakesh ^b  

^a IBM INDIA RESEARCH LAB   (India)

^b IBM ALMADEN RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; CONTEXT SENSITIVE LANGUAGES; FEATURE EXTRACTION; NUMERICAL METHODS; PARAMETER ESTIMATION;

CONTEXT-SENSITIVE LEARNABLE ASYMMETRIC DISSIMILARITY (CLAD); DISSIMILARITY MEASURES;

LEARNING ALGORITHMS;

EID: 34548564264     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.107     Document Type: Conference Paper

References (13)

1. http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo20/www/data/news20. tar.gz.
2. C. C. Aggarwal. Towards systematic design of distance functions for data mining applications. In Proceedings of SIGKDD, pages 9-18. ACM Press, 2003.
3. M. Bilenko, S. Basu, and R. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In Proceedings of ICML, pages 81-88, 2004.
4. D. Conn, R. Caruana, and A. McCallum. Semi-supervised clustering with user feedback. Technical Report TR2003-1892, Cornell University, 2003.
5. M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery. Learning to extract symbolic knowledge from the world wide web. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98), 1998.
6. S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, 1999.
7. T. Joachims. Making large-scale svm learning practical. In B. Schlkopf, C. Surges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT-Press, 1999.
8. T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of SIGKDD, pages 133-142. ACM Press, 2002.
9. K. Krishna and R. Krishnapuram. A clustering algorithm for asymmetrically related data with applications to text mining. In Proceedings of CIKM, pages 571-573. ACM, 2001.
10. S. Kullback. Information Theory and Statistics. Gloucester, MA: Peter Smith, 1968.
11. K. Kummamuru, R. Krishnapuram, and R. Agrawal. Learning spatially variant dissimilarity (svad) measures. In Proceedings of SIGKDD, pages 611-616, 2004.
12. M. Schultz and T. Joachims. Learning a distance metric with relative comparisons. Advances in Neural Information Processing Systems, 16, 2003.
13. N. Slonim and N. Tishby. Document clustering using word clusters via the information bottleneck method. In Proceedings of SIGIR, pages 208-215, 2000.