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Research article summary:
On-line learning in changing environments with applications in supervised and unsupervised learning.
Abstract Extract:
An adaptive on-line algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss ... (Full abstract text below) Published 2002 Jun-Jul
in Journal: Neural Netw
(Language : eng)
Full Pubmed Extract
This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:
1. Neural Netw.
2002 Jun-Jul;15(4-6):743-60
On-line learning in changing environments with applications in supervised and unsupervised learning.
Murata N, Kawanabe M, Ziehe A, Müller KR, Amari S
School of Science and Engineering, Waseda University, Tokyo, Japan.
An adaptive on-line algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is given and the Hessian is not available. The framework is applied for unsupervised and supervised learning. Its efficiency is demonstrated for drifting and switching non-stationary blind separation tasks of acoustic signals. Furthermore applications to classification (US postal service data set) and time-series prediction in changing environments are presented.
PMID : 12371524 [PubMed - Indexed for MEDLINE]
This information is obtained from the National Library of Medicine (NLM). Abstract text and other information may be subject to copyright. Type "NLM copyright" into Google for more information.
Full Author Information
| First Name | LastName | Initials |
| Noboru | Murata | N |
| Motoaki | Kawanabe | M |
| Andreas | Ziehe | A |
| Klaus-Robert | Müller | KR |
| Shun-ichi | Amari | S |
Affiliation: School of Science and Engineering, Waseda University, Tokyo, Japan.
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Category links from this article:- Algorithms
- Animals
- Environment
- Humans
- Learning - physiology
- Models, Biological
- Nerve Net - physiology
- Online Systems
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