Support vector machines with example dependent costs

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2837, Issue , 2003, Pages 23-34

  Brefeld, Ulf ^a   Geibel, Peter ^a   Wysotzki, Fritz ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; COST EFFECTIVENESS; ERROR CORRECTION; LEARNING ALGORITHMS; MATRIX ALGEBRA; NEURAL NETWORKS; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING; VECTORS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COSTS; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES;

COST MATRIX; MEAN PREDICTIVE ERROR; MISCLASSIFICATION COSTS; SUPPORT VECTOR MACHINES (SMV); ARBITRARY COSTS; BAYES RULE; COST-SENSITIVE;

LEARNING SYSTEMS; LEARNING ALGORITHMS;

EID: 9444295412     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39857-8_5     Document Type: Conference Paper

References (11)

1. Ch. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995.
2. C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
3. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines (and Other Kernel-Based Learning Methods). Cambridge University Press, 2000.
4. Charles Elkan. The foundations of Cost-Sensitive learning. In Bernhard Nebel, editor, Proceedings of the seventeenth International Conference on Artificial Intelligence (IJCAI-01), pages 973-978, San Francisco, CA, August 4-10 2001. Morgan Kaufmann Publishers, Inc.
5. M. Kukar and I. Kononenko. Cost-sensitive learning with neural networks. In Henri Prade, editor, Proceedings of the 13th European Conference on Artificial Intelligence (ECAI-98), pages 445-449, Chichester, 1998. John Wiley & Sons.
6. A. Lenarcik and Z. Piasta. Rough classifiers sensitive to costs varying from object to object. In Lech Polkowski and Andrzej Skowron, editors, Proceedings of the 1st International Conference on Rough Sets and Current Trends in Computing (RSCTC-98), volume 1424 of LNAI, pages 222-230, Berlin, June 22-26 1998. Springer.
7. Yi Lin. Support vector machines and the bayes rule in classification. Data Mining and Knowledge Discovery, 6(3):259-275, 2002.
8. Yi Lin, Yoonkyung Lee, and Grace Wahba. Support vector machines for classification in nonstandard situations. Machine Learning, 46(1-3):191-202, 2002.
9. Dragos D. Margineantu and Thomas G. Dietterich. Bootstrap methods for the cost-sensitive evaluation of classifiers. In Proc. 17th International Conf. on Machine Learning, pages 583-590. Morgan Kaufmann, San Francisco, CA, 2000.
10. Lorenza Saitta, editor. Machine Learning - A Technological Roadmap. University of Amsterdam, 2000. ISBN: 90-5470-096-3.
11. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.