Evaluating classifiers by means of test data with noisy labels

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2003, Pages 513-518

  Lam, Chuck P ^a   Stork, David G ^b  

^a Stanford University   (United States)

^b Ricoh Innovations Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIER'S PERFORMANCE; IN-BUILDINGS; NOISY LABELS; TEST PATTERN; TEST SAMPLES; TIME-CONSUMING TASKS; TRAINING AND TESTING; UPPER AND LOWER BOUNDS;

PATTERN RECOGNITION SYSTEMS;

ARTIFICIAL INTELLIGENCE;

EID: 84880835391     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1lth Annual Conference on Computational Learning Theory (COLT '98), pages 92-100, 1998.
2. Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. John Wiley &Sons, 2nd edition, 2001.
3. Elcazar Eskin. Detecting errors within a corpus using anomaly detection. In Proceedings of the First Conference of the North American Association for Computational Linguistics (NAACL-2000), 2000.
4. William B. Frakcs and Ricardo Baeza-Yates, editors. Information Retrieval: Data Structures & Algorithms. Prentice Hall, 1992.
5. Cenk Kaynak and Ethem Alpaydin. Multistage cascading of multiple classifiers: One man's noise is another man's data. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 455-462,2000.
6. Kamal Nigam, Andrew McCallum, Sebastian Thrun, and Tom Mitchell. Text classification from labeled and unlabeled examples. Machine Learning, 39(2): 103-134,2000.
7. Padhraic Smyth. Bounds on the mean classification error rate of multiple experts. Pattern Recognition Letters, 1997.
8. David G. Stork and Chuck P. Lam. Open Mind Animals: Ensuring the quality of data openly contributed over the world wide web. In AAAI Workshop on Learning with Imbalanced Data Sets, pages 4-9, 2000.
9. David G. Stork. The Open Mind Initiative. IEEE Intelligent Systems & Their Applications, 14(3): 19-20, 1999.
10. Martin Szummer and Tommi Jaakkola. Kernel expansions with unlabeled examples. In Advances in Neural Information Processing Systems (NIPS), 2000.