On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data

Proceedings, Twentieth International Conference on Machine Learning

Volumn 2, Issue , 2003, Pages 688-695

  Sebban, Marc ^a   Janodet, Jean Christophe ^a  

^a UNIVERSITÉ JEAN MONNET   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATA THEORY; AUTOMATION; DATA ACQUISITION; DATA REDUCTION; DATABASE SYSTEMS; MERGING; PATTERN MATCHING; SPEECH RECOGNITION; STATISTICAL METHODS;

DETERMINISTIC FINITE AUTOMATION (DFA); NOISE TOLERANCE; NOISY DATA; STATE MERGING ALGORITHM;

LEARNING SYSTEMS;

EID: 1942484753     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Aha, D. (1992). Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. Int. Journal Man-Machine Studies, 267-287.
2. Brodley, C., & Friedl, M. (1996). Identifying and eliminating mislabeled training instances. Thirteenth National Conference on Artificial Intelligence (pp. 799-805).
3. de la Higuera, C. (1997). Characteristic sets for polynomial grammatical inference. Journal of Machine Learning, 27, 125-138.
4. John, G., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. Eleventh Int. Conference on Machine Learning (pp. 121-129).
5. Lang, K., Pearlmutter, B., & Price, R. (1998). Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. Fourth Int. Colloquium on Grammatical Inference (pp. 1-12).
6. Langley, P. (1994). Selection of relevant features in machine learning. AAAI Fall Symp. on Relevance.
7. Oncina, J., & García, P. (1992). Inferring regular languages in polynomial update time, vol. 1 of Machine Perception and Artificial Intelligence, 49-61. World Scientific.
8. Schapire, R., Freund, Y., Bartlett, P., & Lee, W. (1998). Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26, 1651-1686.
9. Sebban, M., Janodet, J.-C., & Yahiaoui, A. (2002). Removing noisy data in grammatical inference (in french). Seventh National Conference on Clustering (pp. 311-314).
10. Sebban, M., & Nock, R. (2000). Instance pruning as an information preserving problem. Seventeenth Int. Conference on Machine Learning (pp. 855-862).
11. Sebban, M., Nock, R., & Lallich, S. (2003). Stopping criterion for boosting-based data reduction techniques: from binary to multiclass problems. Int. Journal of Machine Learning Research, 3, 863-885.
12. Wilson, D., & Martinez, T. (1997). Instance pruning techniques. Fourteenth Int. Conference on Machine Learning (pp. 404-411).