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» Modeling Classification and Inference Learning
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CVPR
2008
IEEE
16 years 6 months ago
The Logistic Random Field - A convenient graphical model for learning parameters for MRF-based labeling
Graphical models are fundamental tools for modeling images and other applications. In this paper, we propose the Logistic Random Field (LRF) model for representing a discrete-valu...
Marshall F. Tappen, Kegan G. G. Samuel, Craig V. D...
FTCGV
2011
122views more  FTCGV 2011»
14 years 8 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert
112
Voted
ECML
1991
Springer
15 years 8 months ago
A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition
This paper presents an approach to domain modeling and knowledge acquisition that consists of a gradual and goal-driven improvement of an incomplete domain model provided by a hum...
Gheorghe Tecuci
JCB
2007
130views more  JCB 2007»
15 years 4 months ago
Bayesian Inference of MicroRNA Targets from Sequence and Expression Data
MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although mu...
Jim C. Huang, Quaid Morris, Brendan J. Frey
ICIG
2009
IEEE
15 years 11 months ago
Discriminative Maximum Margin Image Object Categorization with Exact Inference
Categorizing multiple objects in images is essentially a structured prediction problem: the label of an object is in general dependent on the labels of other objects in the image....
Qinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurman...