Exact indexing for support vector machines

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2011, Pages 709-720

  Yu, Hwanjo ^a   Ko, Ilhwan ^a   Kim, Youngdae ^a   Hwang, Seungwon ^a   Han, Wook Shin ^b  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b Kyungpook National University   (South Korea)

Author keywords

indexing; support vector machines

Indexed keywords

INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; QUERY PROCESSING; SEARCH ENGINES; SUPPORT VECTOR MACHINES;

CLASSIFICATION REGRESSION; INDEXING; INDEXING METHODS; KERNEL PARAMETER; KERNEL SPACE; MACHINE FUNCTION; MACHINE-LEARNING; RANK LEARNING; RANKING FUNCTIONS; SUPPORT VECTORS MACHINE;

FUNCTION EVALUATION;

EID: 79960005590     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1989323.1989398     Document Type: Conference Paper

References (24)

1. T. Bozkaya and M. Ozsoyoglu. Indexing large metric spaces for similarity search queries. ACM Transaction on Database Systems, 2002.
2. N. Bruno, L. Gravano, and A. Marian. Evaluating top-k queries over web-accessible databases. In Proc. Int. Conf. Data Engineering (ICDE'02), 2002.
3. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121-167, 1998.
4. E. Chang and S. Tong. Support vector machine active learning for image retrieval. In ACM Int. Conf. Multimedia (MM'01), 2001.
5. N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer. Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002.
6. N. Christianini and J. Shawe-Taylor. An Introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000.
7. P. Ciaccia, M. Patella, and P. Zezula. M-tree: an efficient access method for similarity search in metric spaces. In Proc. Int. Conf. Very Large Databases (VLDB'97), 1997.
8. R. Fagin, A. Lote, and M. Naor. Optimal aggregation algorithms for middleware. In Proc. ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems (PODS'01), 2001.
9. U. Guentzer, W. Balke, and W. Kiessling. Optimizing multi-feature queries in image databases. In Proc. Int. Conf. Very Large Databases (VLDB'00), 2000.
10. S.-W. Hwang and K. C.-C. Chang. Optimizing access cost for top-k queries over web sources: A unified cost-based approach. In Proc. Int. Conf. Data Engineering (ICDE'05), 2005.
11. H. Jagadish, B. Ooi, K. Tan, C. Yu, and R. Zhang. idistance: An adaptive b+-tree based indexing method for nearest neighbor search. ACM Transaction on Database Systems, 2005.
12. D. Keim. Tutorial on high-dimensional index structures: Database support for next decades applications. In Proc. Int. Conf. Data Engineering (ICDE'00), 2000.
13. N. Panda and E. Chang. Exploiting geometric property for support vector machine indexing. In SIAM Int. Conf. Data Mining (SDM'05), 2005.
14. N. Panda and E. Chang. Efficient top-k hyperplane query processing for multimedia information retrieval. In ACM Int. Conf. Multimedia (MM'06), 2006.
15. N. Panda and E. Chang. Kdx: An indexer for support vector machines. IEEE Transactions on Knowledge and Data Engineering, 2006.
16. J. Peng and D. Heisterkamp. Kernel indexing for relevance feedback image retrieval. In Proc. Int. Conf. Image Processing (ICIP'03), 2003.
17. V. Vapnik. Statistical Learning Theory. John Wiley and Sons, 1998.
18. D. Xin, J. Han, and K. Chang. Progressive and selective merge: Computing top-k with ad-hoc ranking functions. In Proc. ACM Int. Conf. Management of Data (SIGMOD'07), 2007.
19. H. Yu. SVM selective sampling for ranking with application to data retrieval. In Proc. Int. Conf. Knowledge Discovery and Data Mining (KDD'05), 2005.
20. H. Yu. Selective sampling techniques for feedback-based data retrieval. Data Mining and Knowledge Discovery, 2010.
21. H. Yu, S. Hwang, and K. C.-C. Chang. RankFP: A framework for supporting rank formulation and processing. In Proc. Int. Conf. Data Engineering (ICDE'05), 2005.
22. H. Yu, S.-W. Hwang, and K. C.-C. Chang. Enabling soft queries for data retrieval. Information Systems, 2007.
23. H. Yu, T. Kim, J. Oh, I. Ko, S. Kim, and W. Han. Enabling multi-level relevance feedback on pubmed by integrating rank learning into dbms. BMC Bioinformatics, 2010.
24. Z. Zhang, S.-W. Hwang, K. C.-C. Chang, M. Wang, C. A. Lang, and Y.-C. Chang. Boolean + ranking: Querying a database by k-constrained optimization. In Proc. ACM Int. Conf. Management of Data (SIGMOD'06), 2006.