Adapting SVM classifiers to data with shifted distributions

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2007, Pages 69-74

  Yang, Jun ^a   Yan, Rong ^b   Hauptmann, Alexander G ^a  

^a Carnegie Mellon University   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SUPPORT; BASELINE METHODS; CLASSIFICATION ERRORS; DATA MINING APPLICATIONS; DATA SETS; INTERNATIONAL CONFERENCES; LOSS MINIMIZATION; OBJECTIVE FUNCTION; SELECTIVE SAMPLING; SVM CLASSIFIERS; VIDEO CLASSIFICATION;

ADMINISTRATIVE DATA PROCESSING; BENCHMARKING; CLASSIFIERS; DATA MINING; DECISION SUPPORT SYSTEMS; IMAGE SEGMENTATION; INFORMATION MANAGEMENT; KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; MINING; SEARCH ENGINES; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

EID: 49549114434     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2007.37     Document Type: Conference Paper

References (11)

1. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
2. C. Campbell, N. Cristianini, and A. Smola. Query learning with large margin classifiers. In Proc. of ICML, 2000.
3. R. Klinkenberg and T. Joachims. Detecting concept drift with support vector machines. In Proc. of ICML, pages 487-494, 2000.
4. J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: A new ensemble method for tracking concept drift. In Proc. of ICDM, page 123, 2003.
5. X. Liao, Y. Xue, and L. Carin. Logistic regression with an auxiliary data source. In Proc. of ICML, pages 505-512, 2005.
6. J. Platt. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods: support vector learning, 1999.
7. A. Smeaton and P. Over. Trecvid: Benchmarking the effectiveness of infomration retrieval tasks on digital video. In Proc. of CIVR, 2003.
8. N. Syed, H. Liu, and K. Sung. Incremental learning with support vector machines. In In Workshop on Support Vector Machines, at the IJCAI, 1999.
9. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proc. of ICML, pages 999-1006, 2000.
10. H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proc. of SIGKDD, pages 226-235, 2003.
11. P. Wu and T. G. Dietterich. Improving SVM accuracy by training on auxiliary data sources. In Proc. of ICML, page 110, 2004.