| Item type |
デフォルトアイテムタイプ_（フル）(1) |
| 公開日 |
2023-03-18 |
| タイトル |
|
|
タイトル |
A Recurrent Log-Linearized Gaussian Mixture Network |
|
言語 |
en |
| 作成者 |
Tsuji, Toshio
Bu, Nan
Fukuda, Osamu
Kaneko, Makoto
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
権利情報 |
Copyright (c) 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
EEG |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Gaussian mixture model |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
hidden Markov model (HMM) |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
log-linearized model |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
neural networks (NNs) |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
pattern classification |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
recurrent neural networks (RNNs) |
| 主題 |
|
|
主題Scheme |
NDC |
|
主題 |
530 |
| 内容記述 |
|
|
内容記述 |
Context in time series is one of the most useful andinteresting characteristics for machine learning. In some cases, thedynamic characteristic would be the only basis for achieving a possibleclassification. A novel neural network, which is named “a recurrentlog-linearized Gaussian mixture network (R-LLGMN)," isproposed in this paper for classification of time series. The structureof this network is based on a hidden Markov model (HMM),which has been well developed in the area of speech recognition.R-LLGMN can as well be interpreted as an extension of a probabilisticneural network using a log-linearized Gaussian mixturemodel, in which recurrent connections have been incorporated tomake temporal information in use. Some simulation experimentsare carried out to compare R-LLGMN with the traditional estimatorof HMM as classifiers, and finally, pattern classification experimentsfor EEG signals are conducted. It is indicated from theseexperiments that R-LLGMN can successfully classify not only artificialdata but real biological data such as EEG signals. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
IEEE |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1109/TNN.2003.809403 |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
http://dx.doi.org/10.1109/TNN.2003.809403 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
1045-9227 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA10736045 |
| 開始ページ |
|
|
開始ページ |
304 |
| 書誌情報 |
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
巻 14,
号 2,
p. 304-316,
発行日 2003
|
| 旧ID |
14212 |
IEEE_TNN_14_2_304-316_2003.pdf (860.8 KB)
