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ICML
1998
IEEE
16 years 2 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
116
Voted
DATAMINE
2006
157views more  DATAMINE 2006»
15 years 1 months ago
Data Clustering with Partial Supervision
Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semisupervised clustering algo...
Abdelhamid Bouchachia, Witold Pedrycz
111
Voted
ICML
2006
IEEE
16 years 2 months ago
The support vector decomposition machine
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
Francisco Pereira, Geoffrey J. Gordon
135
Voted
CVPR
2012
IEEE
13 years 4 months ago
Multi-class cosegmentation
Bottom-up, fully unsupervised segmentation remains a daunting challenge for computer vision. In the cosegmentation context, on the other hand, the availability of multiple images ...
Armand Joulin, Francis Bach, Jean Ponce
126
Voted
MICCAI
2007
Springer
16 years 2 months ago
Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree
Abstract. Automatic delineation and robust measurement of fetal anatomical structures in 2D ultrasound images is a challenging task due to the complexity of the object appearance, ...
Gustavo Carneiro, Bogdan Georgescu, Sara Good, Dor...