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» The Use of Classifiers in Sequential Inference
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JIB
2006
220views more  JIB 2006»
14 years 9 months ago
An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions
Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from hi...
Fiona Browne, Haiying Wang, Huiru Zheng, Francisco...
CVPR
2012
IEEE
13 years 2 days ago
Robust visual tracking using autoregressive hidden Markov Model
Recent studies on visual tracking have shown significant improvement in accuracy by handling the appearance variations of the target object. Whereas most studies present schemes ...
Dong Woo Park, Junseok Kwon, Kyoung Mu Lee
63
Voted
ISMB
1997
14 years 11 months ago
Enumerating and Ranking Discrete Motifs
Discrete motifsthat discriminate functionalclasses of proteins are useful for classifying newsequences, capturingstructural constraints, andidentifyingprotein subclasses.Despiteth...
Craig G. Nevill-Manning, Komal S. Sethi, Thomas D....
CVPR
1999
IEEE
15 years 11 months 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...
95
Voted
PAMI
2008
175views more  PAMI 2008»
14 years 9 months ago
Discriminative Feature Co-Occurrence Selection for Object Detection
This paper describes an object detection framework that learns the discriminative co-occurrence of multiple features. Feature co-occurrences are automatically found by Sequential F...
Takeshi Mita, Toshimitsu Kaneko, Björn Stenge...