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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yamazaki, K. |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G5-19, 4259 Nagatsuta, Midori-ku, Yokohama, Japan (Yamazaki, K.) |
| Abstract | Hierarchical statistical models are widely applied to information science and data engineering. The models consist of two variables: an observable variable for the given data and a latent variable for an unobservable label. There are a lot of analysis results on the generalization error measuring the prediction accuracy of the observation variable. However, the accuracy of estimation for the latent variable has not been studied well. In the previous study, an error function for the latent variable was formulated, and the asymptotic behavior was analyzed on the maximum likelihood estimation. The present paper extends the analysis method to the semi-supervised learning, where the labels are available in some parts of data, and reveals the asymptotic form of the error function. |
| Starting Page | 707 |
| Ending Page | 711 |
| File Size | 111804 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467325219 |
| e-ISBN | 9784885522673 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | IEICE Institute of Electronics Informati |
| Subject Keyword | Maximum likelihood estimation Accuracy Computational modeling Hidden Markov models Semisupervised learning Data models |
| Content Type | Text |
| Resource Type | Article |
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