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» Learning Subjective Functions with Large Margins
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Publication
173views
14 years 3 months ago
Max-Flow Segmentation of the Left Ventricle by Recovering Subject-Specific Distributions via a Bound of the Bhattacharyya Measur
This study investigates fast detection of the left ventricle (LV) endo- and epicardium boundaries in a cardiac magnetic resonance (MR) sequence following the optimization of two or...
Ismail Ben Ayed, Hua-mei Chen, Kumaradevan Punitha...
159
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JMLR
2012
13 years 7 months ago
Structured Output Learning with High Order Loss Functions
Often when modeling structured domains, it is desirable to leverage information that is not naturally expressed as simply a label. Examples include knowledge about the evaluation ...
Daniel Tarlow, Richard S. Zemel
ICPR
2008
IEEE
16 years 5 months ago
Prototype learning with margin-based conditional log-likelihood loss
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum...
Cheng-Lin Liu, Xiaobo Jin, Xinwen Hou
197
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SIGIR
2011
ACM
14 years 7 months ago
Utilizing marginal net utility for recommendation in e-commerce
Traditional recommendation algorithms often select products with the highest predicted ratings to recommend. However, earlier research in economics and marketing indicates that a ...
Jian Wang, Yi Zhang
181
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CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 10 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson