Article thumbnail

A New HMM training and testing scheme

By Albert Hung-ren Ko, Robert Sabourin and Alceu De Souza Britto

Abstract

One of disadvantages of Hidden Markov Models (HMMs) is its low resistance to unexpected noises among observation sequences. Unexpected noises in a sequence usually "break " a sequence of observations, and then makes this sequence unrecognizable for trained models. We propose a new HMM training and testing scheme, which compensates some of the negative effects of such noises. We carried out experiment on handwritten digit recognition problem and the result suggests our proposal can be as effective as multiclassifier systems. 1

Year: 2012
OAI identifier: oai:CiteSeerX.psu:10.1.1.214.5131
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://figment.cse.usf.edu/~sf... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.