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Journal article

Learning and classification of complex dynamics

Abstract:

Standard, exact techniques based on likelihood maximization are available for learning Auto-Regressive Process models of dynamical processes. The uncertainty of observations obtained from real sensors means that dynamics can be observed only approximately. Learning can still be achieved via 'EM-K'-Expectation-Maximization (EM) based on Kalman Filtering. This cannot handle more complex dynamics, however, involving multiple classes of motion. A problem arises also in the case of dynamical proce...

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Publication status:
Published

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Publisher copy:
10.1109/34.877523

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
Publisher:
IEEE
Journal:
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume:
22
Issue:
9
Pages:
1016-1034
Publication date:
2000-09-01
DOI:
ISSN:
0162-8828
Source identifiers:
439039
Language:
English
Keywords:
Pubs id:
pubs:439039
UUID:
uuid:d0f63b2a-bcc3-416b-9e28-457112ed8946
Local pid:
pubs:439039
Deposit date:
2014-02-08

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