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We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learn robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time- ...
Mobile users expose their location to potentially untrusted entities by using location-based services. Based on the frequency of location exposure in these applications, we divide them into two main types: Continuous and Sporadic. These two location exposu ...
This paper shows that Hidden Markov Models (HMMs) can be effectively ap- plied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian Mixture Model (GMM) technique. Experi- ments conducted on the Face Recogn ...
In this paper, we propose a Markov chain modeling of complicated phenomena observed from coupled chaotic oscillators. Once we obtain the transition probability matrix from computer simulation results, various statistical quantities can be easily calculated ...
We study the convergence of Markov Decision Processes made of a large number of objects to optimization problems on ordinary differential equations (ODE). We show that the optimal reward of such a Markov Decision Process, satisfying a Bellman equation, con ...
The dynamics of sales opportunities can be modelled by a Markov Decision Process. The latter can be solved by using dynamic programming and assigns to each state an optimal action. In this project, states are modelled by the number of opportunities at five ...
Formal analysis techniques are widely used today in order to verify and analyze communication protocols. In this work, we launch a quantitative analysis for the low-cost Radio Frequency Identification (RFID) protocol proposed by Song and Mitchell. The anal ...
Ieee Computer Soc Press, Customer Service Center, Po Box 3014, 10662 Los Vaqueros Circle, Los Alamitos, Ca 90720-1264 Usa2011
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we ap ...
Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. ...
We present a probabilistic approach to learn robust models of human motion through imitation. The association of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundancies across multiple demonstrat ...
Institute of Electrical and Electronics Engineers2010