Publication: A novel training method for PHMMs
Program
KU-Authors
KU Authors
Co-Authors
Akman, Arda
Ergüt, Salih
Advisor
Publication Date
2012
Language
English
Type
Conference proceeding
Journal Title
Journal ISSN
Volume Title
Abstract
This paper proposes a novel estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the “achievable margin” defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to different training conditions.
Description
Source:
2012 3rd International Workshop on Cognitive Information Processing, CIP 2012
Publisher:
IEEE
Keywords:
Subject
Engineering, Electrical and electronics engineering