Details
| Original language | English |
|---|---|
| Title of host publication | IV 2015 |
| Subtitle of host publication | 2015 IEEE Intelligent Vehicles Symposium |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 380-385 |
| Number of pages | 6 |
| ISBN (electronic) | 9781467372664 |
| Publication status | Published - 26 Aug 2015 |
| Event | IEEE Intelligent Vehicles Symposium, IV 2015 - Seoul, Korea, Republic of Duration: 28 Jun 2015 → 1 Jul 2015 |
Publication series
| Name | IEEE Intelligent Vehicles Symposium, Proceedings |
|---|---|
| Volume | 2015-August |
Abstract
This paper establishes a duality between the calculus of variations, an increasingly common method for trajectory planning, and Hidden Markov Models (HMMs), a common probabilistic graphical model with applications in artificial intelligence and machine learning. This duality allows findings from each field to be applied to the other, namely providing an efficient and robust global optimization tool and machine learning algorithms for variational problems, and fast local solution methods for large state-space HMMs.
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Automotive Engineering
- Computer Science(all)
- Computer Science Applications
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IV 2015: 2015 IEEE Intelligent Vehicles Symposium. Institute of Electrical and Electronics Engineers Inc., 2015. p. 380-385 7225715 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2015-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Correspondence between variational methods and Hidden Markov Models
AU - Ziehn, Jens
AU - Ruf, M.
AU - Rosenhahn, Bodo
AU - Willersinn, D.
AU - Beyerer, J.
AU - Gotzig, H.
PY - 2015/8/26
Y1 - 2015/8/26
N2 - This paper establishes a duality between the calculus of variations, an increasingly common method for trajectory planning, and Hidden Markov Models (HMMs), a common probabilistic graphical model with applications in artificial intelligence and machine learning. This duality allows findings from each field to be applied to the other, namely providing an efficient and robust global optimization tool and machine learning algorithms for variational problems, and fast local solution methods for large state-space HMMs.
AB - This paper establishes a duality between the calculus of variations, an increasingly common method for trajectory planning, and Hidden Markov Models (HMMs), a common probabilistic graphical model with applications in artificial intelligence and machine learning. This duality allows findings from each field to be applied to the other, namely providing an efficient and robust global optimization tool and machine learning algorithms for variational problems, and fast local solution methods for large state-space HMMs.
UR - http://www.scopus.com/inward/record.url?scp=84951181952&partnerID=8YFLogxK
U2 - 10.1109/ivs.2015.7225715
DO - 10.1109/ivs.2015.7225715
M3 - Conference contribution
AN - SCOPUS:84951181952
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 380
EP - 385
BT - IV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Intelligent Vehicles Symposium, IV 2015
Y2 - 28 June 2015 through 1 July 2015
ER -