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» Learning Subjective Functions with Large Margins
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Publication
173views
13 years 10 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...
JMLR
2012
13 years 2 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 27 days 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
SIGIR
2011
ACM
14 years 2 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
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 7 months 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