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JMLR
2010
202views more  JMLR 2010»
13 years 2 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...
IEEEIAS
2007
IEEE
14 years 2 months ago
Cyber Threat Trend Analysis Model Using HMM
Prevention, not reaction, is normally recognized as one of the best defense strategy against malicious hackers or attackers. The desire of deploying better prevention mechanism mo...
Do-Hoon Kim, Taek Lee, Sung-Oh David Jung, Hoh Pet...
EMNETS
2007
13 years 11 months ago
An HMM framework for optimal sensor selection with applications to BSN sensor glove design
Laparoscopic surgical training is a challenging task due to the complexity of instrument control and demand on manual dexterity and hand-eye coordination. Currently, training and ...
Rachel C. King, Louis Atallah, Ara Darzi, Guang-Zh...
ECAI
2004
Springer
14 years 1 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
LCN
2006
IEEE
14 years 1 months ago
Modelling Voice Communication in Disaster Area Scenarios
This paper deals with voice communication models for disaster area scenarios. The goal is to design models that can be used to generate realistic push to talk traffic for single ...
Nils Aschenbruck, Michael Gerharz, Matthias Frank,...