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NIPS
2008
11 years 2 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
PERCOM
2007
ACM
12 years 1 months ago
Sensor Scheduling for Optimal Observability Using Estimation Entropy
We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process ...
Mohammad Rezaeian
ICML
2008
IEEE
12 years 2 months ago
Laplace maximum margin Markov networks
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
Jun Zhu, Eric P. Xing, Bo Zhang
CVIU
2006
222views more  CVIU 2006»
11 years 1 months ago
Conditional models for contextual human motion recognition
We present algorithms for recognizing human motion in monocular video sequences, based on discriminative Conditional Random Field (CRF) and Maximum Entropy Markov Models (MEMM). E...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...
ICASSP
2008
IEEE
11 years 8 months ago
Link throughput of multi-channel opportunistic access with limited sensing
—We aim to characterize the maximum link throughput of a multi-channel opportunistic communication system. The states of these channels evolve as independent and identically dist...
Keqin Liu, Qing Zhao
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