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IROS
2007
IEEE
129views Robotics» more  IROS 2007»
15 years 4 months ago
Representability of human motions by factorial hidden Markov models
— This paper describes an improved methodology for human motion recognition and imitation based on Factorial Hidden Markov Models (FHMM). Unlike conventional Hidden Markov Models...
Dana Kulic, Wataru Takano, Yoshihiko Nakamura
PR
2011
14 years 4 months ago
A variational Bayesian methodology for hidden Markov models utilizing Student's-t mixtures
The Student’s-t hidden Markov model (SHMM) has been recently proposed as a robust to outliers form of conventional continuous density hidden Markov models, trained by means of t...
Sotirios Chatzis, Dimitrios I. Kosmopoulos
JSSPP
2004
Springer
15 years 3 months ago
Parallel Computer Workload Modeling with Markov Chains
Abstract. In order to evaluate different scheduling strategies for parallel computers, simulations are often executed. As the scheduling quality highly depends on the workload tha...
Baiyi Song, Carsten Ernemann, Ramin Yahyapour
AMC
2008
99views more  AMC 2008»
14 years 10 months ago
Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of c...
Roderick V. N. Melnik
ECML
2006
Springer
15 years 1 months ago
PAC-Learning of Markov Models with Hidden State
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...
Ricard Gavaldà, Philipp W. Keller, Joelle P...