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Use of unlabeled time series data in hidden Markov models by Masashi Inoue; Naonori Ueda is a Computer Science article available to read on EtoBox.

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## Abstract In this paper, the authors propose a method of assigning probabilistic class labels to unlabeled time‐series data that can be collected inexpensively to use together with labeled data as training data in order to improve the performance of hidden Markov models (HMM) as classifiers. Conventional methods of using unlabeled data according to probabilistic class labels were only for static data. They could not deal with time‐series data, which are used with HMMs. Therefore, the authors extended the conventional methods as follows. First, they formed extended tied‐mixture hidden Markov models (ETM‐HMMs) by mixing multiple HMMs that exist independently for each class. Then, they introduced the extended Baum‐Welch (EBW) algorithm as the learning algorithm from time‐series data in which labeled and unlabeled data coexist in the ETM‐HMMs. The authors then assumed a situation in which there existed insufficient labeled training data and used the proposed method to perform classification experiments for Japanese sign language data and voice data. When unlabeled data were added to the labeled training data according to the proposed method, the classification error rate for Japanese

Who reads Use of unlabeled time series data in hidden Markov models?

Author

Masashi Inoue; Naonori Ueda

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0882-1666)

Published

2003

Language

EN

Field

Computer Science (Physical Sciences)