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» Incremental Construction of Structured Hidden Markov Models
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NIPS
1998
14 years 3 days ago
An Entropic Estimator for Structure Discovery
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
Matthew Brand
ICIP
2004
IEEE
15 years 11 days ago
Discovering meaningful multimedia patterns with audio-visual concepts and associated text
The work presents the first effort to automatically annotate the semantic meanings of temporal video patterns obtained through unsupervised discovery processes. This problem is in...
Lexing Xie, Lyndon S. Kennedy, Shih-Fu Chang, Ajay...
PRDC
2006
IEEE
14 years 4 months ago
Detecting and Exploiting Symmetry in Discrete-state Markov Models
Dependable systems are usually designed with multiple instances of components or logical processes, and often possess symmetries that may be exploited in model-based evaluation. T...
W. Douglas Obal II, Michael G. McQuinn, William H....
CORR
2011
Springer
214views Education» more  CORR 2011»
13 years 2 months ago
Convex Approaches to Model Wavelet Sparsity Patterns
Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as d...
Nikhil S. Rao, Robert D. Nowak, Stephen J. Wright,...
CVPR
1999
IEEE
15 years 23 days ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...