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» Hidden Markov Models with Multiple Observation Processes
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AVSS
2006
IEEE
16 years 11 days ago
Nonparametric Background Modeling Using the CONDENSATION Algorithm
Background modeling for dynamic scenes is an important problem in the context of real time video surveillance systems. Several nonparametric background models have been proposed t...
Xingzhi Luo, Suchendra M. Bhandarkar, Wei Hua, Hai...
IUI
2004
ACM
15 years 11 months ago
Sheepdog: learning procedures for technical support
Technical support procedures are typically very complex. Users often have trouble following printed instructions describing how to perform these procedures, and these instructions...
Tessa A. Lau, Lawrence D. Bergman, Vittorio Castel...
JMLR
2010
202views more  JMLR 2010»
15 years 1 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
ICPP
1999
IEEE
15 years 10 months ago
Performance Study of Token-Passing Protocol for Traffic Multiplicity in Optical Networks
This paper extended a mathematical technique to model the behaviour of token-passing protocol in a star-coupled wavelength-division multiplexing (WDM) optical network for traffic ...
S. Selvakennedy, Ashwani K. Ramani
UAI
2000
15 years 7 months ago
PEGASUS: A policy search method for large MDPs and POMDPs
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...
Andrew Y. Ng, Michael I. Jordan