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ICML
2008
IEEE
14 years 8 months ago
Composite kernel learning
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Mul...
Marie Szafranski, Yves Grandvalet, Alain Rakotomam...
CVPR
2000
IEEE
13 years 12 months ago
Learning Patterns from Images by Combining Soft Decisions and Hard Decisions
We present a novel approach for learning patterns (sub-images) shared by multiple images without prior knowledge about the number and the positions of the patterns in the images. ...
Pengyu Hong, Thomas S. Huang, Roy Wang
AAAI
2012
11 years 10 months ago
Towards Discovering What Patterns Trigger What Labels
In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input spa...
Yu-Feng Li, Ju-Hua Hu, Yuang Jiang, Zhi-Hua Zhou
ICCV
2011
IEEE
12 years 7 months ago
Decision Tree Fields
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fiel...
Sebastian Nowozin, Carsten Rother, Shai Bagon, Ban...
JMLR
2012
11 years 10 months ago
Perturbation based Large Margin Approach for Ranking
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...
Eunho Yang, Ambuj Tewari, Pradeep D. Ravikumar