Classifier adaptation with non-representative training data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2423, Issue , 2002, Pages 123-133

  Veeramachaneni, Sriharsha ^a   Nagy, George ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

ADAPTIVE METHODOLOGIES; DECISION BOUNDARY; DIGIT RECOGNITION; FEATURE SPACE; GAUSSIAN QUADRATIC CLASSIFIERS; STYLE CONSTRAINED CLASSIFICATION; TRAINING DATA; UNSUPERVISED ADAPTATION;

CLASSIFICATION (OF INFORMATION);

EID: 84947779342     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45869-7_17     Document Type: Conference Paper

References (10)

1. C. J. Leggetter and P. C. Woodland. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markovmo dels. Computer Speech and Language, 9(2): 171-185, April 1995.
2. V. Castelli and T. M. Cover. The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Transactions on Information Theory, 42: 2102-2117, November 1996.
3. G. Nagy and G. L. Shelton Jr. Self-corrective character recognition system. IEEE Transactions on Information Theory, IT-12(2): 215-222, April 1966.
4. H. S. Baird and G. Nagy. A self-correcting 100-font classifier. In L. Vincent and T. Pavlidis, editors, Document Recognition, Proceedings of the SPIE, volume 2181, pages 106-115, 1994.
5. P. Sarkar. Style consistency in pattern fields. PhD thesis, Rensselaer Polytechnic Institute, Troy, NY, 2000.
6. P. Sarkar and G. Nagy. Style consistency in isogenous patterns. In Proceedings of the Sixth International Conference on Document Analysis and Recognition, pages 1169-1174, 2001.
7. S. Veeramachaneni, H. Fujisawa, C.-L. Liu, and G. Nagy. Style-conscious quadratic field classifier. In Proceedings of the Sixteenth International Conference on Pattern Recognition, 2002. (Accepted).
8. C. Mathis and T. Breuel. Classification using a Hierarchical Bayesian Approach. Submitted for publication.
9. C.L. Liu, H. Sako, and H. Fujisawa. Performance evaluation of pattern classifiers for handwritten character recognition. International Journal on Document Analysis and Recognition, 4(3): 191-204, 2002.
10. P. Grother. Handprinted forms and character database, NIST special database 19, March 1995. Technical Report and CDROM.