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» Hidden Markov Models with Multiple Observation Processes
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IDA
2003
Springer
15 years 7 months ago
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Abstract. Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any mode...
Allan Tucker, Xiaohui Liu
EDCC
1999
Springer
15 years 6 months ago
Dependability Modelling and Sensitivity Analysis of Scheduled Maintenance Systems
Abstract. In this paper we present a new modelling approach for dependability evaluation and sensitivity analysis of Scheduled Maintenance Systems, based on a Deterministic and Sto...
Andrea Bondavalli, Ivan Mura, Kishor S. Trivedi
IVC
2007
173views more  IVC 2007»
15 years 1 months ago
Outdoor recognition at a distance by fusing gait and face
We explore the possibility of using both face and gait in enhancing human recognition at a distance performance in outdoor conditions. Although the individual performance of gait ...
Zongyi Liu, Sudeep Sarkar
ICASSP
2011
IEEE
14 years 5 months ago
Deep Belief Networks using discriminative features for phone recognition
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of fea...
Abdel-rahman Mohamed, Tara N. Sainath, George Dahl...
CDC
2008
IEEE
118views Control Systems» more  CDC 2008»
15 years 8 months ago
A density projection approach to dimension reduction for continuous-state POMDPs
Abstract— Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on discrete-state models, and these algorithms ...
Enlu Zhou, Michael C. Fu, Steven I. Marcus