A new formalization of minimum classification error using a Parzen estimate of classification chance

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 2003, Pages 713-716

  McDermott, Erik ^a   Katagiri, Shigeru ^a  

^a NTT Communication Science Laboratories   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; ERROR ANALYSIS; PATTERN RECOGNITION;

PARZEN KERNEL;

SIGNAL PROCESSING;

EID: 0141565146     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Biem, A. (2001). Minimum Classification Error Training of Hidden Markov Models for Handwriting Recognition. Proceedings of the IEEE ICASSP.
2. Duda, R.O. and Hart, P.E. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons.
3. Juang, B.-H. and Katagiri, S. (1992). Discriminative Learning for Minimum Error Classification. IEEE Transactions on Signal Processing, Vol. 40, No. 12, pp. 3043-3053.
4. Katagiri, S., Lee, C.-H. and Juang, B.-H. (1991). New Discriminative Training Algorithms Based on the Generalized Descent Method. Proc. 1991 IEEE Workshop on Neural Networks for Signal Processing, pp. 299-308.
5. Katagiri, S., Juang, B.-H. and Lee, C.-H. (1998). Pattern Recognition Using a Family of Design Algorithms based upon the Generalized Probabilistic Descent Method. Proc. IEEE, Vol. 86, No. 11, pp. 2345-2373.
6. McDermott, E. (1997). Discriminative Training for Speech Recognition. Doctoral thesis, Waseda University, Tokyo.
7. McDermott, E., Biem, A., Tenpaku, S., and Katagiri, S. (2000). Discriminative Training for Large Vocabulary Telephone-based Name Recognition. Proc. of the IEEE ICASSP.
8. McDermott, E. and Katagiri, S. (2002). A Parzen window based derivation of Minimum Classification Error from the theoretical Bayes classification risk. Proc. of ICSLP.
9. McDermott, E. and Katagiri, S. (2002). Minimum Classification Error via a Parzen window based estimate of the theoretical Bayes classification risk Proc. of the IEEE Workshop on Neural Networks for Signal Processing, pp. 415-424.