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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Authors
Bibliographic Details
- 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
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:439039
- UUID:
-
uuid:d0f63b2a-bcc3-416b-9e28-457112ed8946
- Local pid:
- pubs:439039
- Deposit date:
- 2014-02-08
Terms of use
- Copyright date:
- 2000
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