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» Maximum Likelihood Identification of a Dynamic Robot Model
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TASLP
2002
96views more  TASLP 2002»
13 years 10 months ago
MAP speaker adaptation of state duration distributions for speech recognition
This paper presents a framework for maximum a posteriori (MAP) speaker adaptation of state duration distributions in hidden Markov models (HMM). Four key issues of MAP estimation, ...
Néstor Becerra Yoma, Jorge Silva Sán...
ICML
2000
IEEE
14 years 11 months ago
On-line Learning for Humanoid Robot Systems
Humanoid robots are high-dimensional movement systems for which analytical system identification and control methods are insufficient due to unknown nonlinearities in the system s...
Gaurav Tevatia, Jörg Conradt, Sethu Vijayakum...
CDC
2008
IEEE
171views Control Systems» more  CDC 2008»
14 years 5 months ago
Constrained optimal control theory for differential linear repetitive processes
Abstract. Differential repetitive processes are a distinct class of continuous-discrete twodimensional linear systems of both systems theoretic and applications interest. These pr...
Michael Dymkov, Eric Rogers, Siarhei Dymkou, Krzys...
CRV
2009
IEEE
237views Robotics» more  CRV 2009»
14 years 5 months ago
SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice Segmentation
The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment ...
Alexander Wong, David A. Clausi, Paul W. Fieguth
NIPS
1998
14 years 6 days ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis