A HMM training algorithm with query-based learning for refinement of classification boundary

Author

Park, Dong-Chul ; Jung, Jio ; Moon, Seok-Yong ; Cho, Yong

Author_Institution

Intelligent Comput. Res. Lab., MyongJi Univ., South Korea

Volume

5

fYear

1999

fDate

1999

Firstpage

3063

Abstract

A training algorithm of hidden Markov model (HMM) using query-based learning is proposed and applied to the recognition of isolated digits in this paper. An efficient query learning procedure is designed to provide the good training data to the oracle in query-based learning at low cost. The proposed algorithm uses the concept that stems from the gradient based inversion algorithm of artificial neural networks. The proposed algorithm is compared with conventional training methods on isolated digit recognition problem. The results show that the proposed query-based HMM learning algorithm can decrease the recognition error rate up to 60% in our experiments

Keywords

character recognition; hidden Markov models; learning (artificial intelligence); neural nets; pattern classification; character recognition; classification boundary refinement; hidden Markov model; neural networks; pattern classification; query-based learning; Artificial neural networks; Costs; Error analysis; H infinity control; Hidden Markov models; Moon; Speech recognition; Telecommunication computing; Training data; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.836047

Filename

836047