Incorporating Diversity in Active Learning with Support Vector Machines

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 59-66

  Blinker, Klaus ^a  

^a UNIVERSITY OF PADERBORN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY VALUE PROBLEMS; CHARACTER RECOGNITION; COMPUTATIONAL METHODS; FUNCTIONS; INTELLIGENT NETWORKS; ITERATIVE METHODS; MATHEMATICAL MODELS; QUADRATIC PROGRAMMING;

ACTIVE LEARNING; BAYES POINT MACHINES; LEARNING PROCESSES; SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS;

EID: 1942517333     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases. Data available at http://www.ics.uci.edu/~mlearn/ MLRepository.html.
2. Campbell, C., Cristianini, N., & Smola, A. (2000). Query learning with large margin classifiers. Proceedings of the Seventeenth International Conference on Machine Learning, 111-118.
3. Herbrich, R., Graepel, T., & Campbell, C. (2001). Bayes point machines. Journal of Machine Learning Research, 1, 245-279.
4. Hsu, C.-W., & Lin, C.-J. (2002). A simple decomposition method for support vector machines. Machine Learning, 46, 291-314. Implementation available at http://www.csie.ntu.edu.tw/~cjlin/bsvm/.
5. Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification. Ellis Horwood. Data available at ftp.ncc.up.pt/pub/statlog/.
6. Mitchell, T. M. (1982). Generalization as search. Artificial Intelligence, 18, 203-226.
7. Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods - Support Vector Learning (pp. 185-208). Cambridge, MA: MIT Press.
8. Ruján, P., & Marchand, M. (2000). Computing the bayes kernel classifier. Advances in Large Margin Classifiers (pp. 329-348). Cambridge, MA: MIT Press.
9. Schohn, G., & Cohn, D. (2000). Less is more: Active learning with support vector machines. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 839-846). Morgan Kaufmann, San Francisco, CA.
10. Schökopf, B., & Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
11. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. Proceedings of the Fifth Workshop on Computaional Learning Theory (pp. 287-294). San Mateo, CA: Morgan Kaufmann.
12. Shawe-Taylor, J., & Cristianini, N. (1999). Further results on the margin distribution. Proceedings of the twelfth annual conference on Computational learning theory (pp. 278-285). Santa Cruz, CA: ACM Press.
13. Tong, S., & Koller, D. (2000). Support vector machine active learning with applications to text classification. Proceedings of the Seventeenth International Conference on Machine Learning (pp. 999-1006). Morgan Kaufmann, San Francisco, CA.
14. Vapnik, V. (1998). Statistical learning theory. N.Y.: John Wiley.
15. Warmuth, M. K., Rätsch, G., Mathieson, M., Liao, J., & Lemmen, C. (2002). Active learning in the drug discovery process. Advances in Neural information processings systems.