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» A Boosting Approach to Multiple Instance Learning
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ICDM
2008
IEEE
182views Data Mining» more  ICDM 2008»
14 years 2 months ago
Multiple-Instance Regression with Structured Data
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) op...
Kiri L. Wagstaff, Terran Lane, Alex Roper
HUMO
2007
Springer
14 years 1 months ago
Boosted Multiple Deformable Trees for Parsing Human Poses
Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additiona...
Yang Wang 0003, Greg Mori
CVPR
2007
IEEE
14 years 9 months ago
A boosting regression approach to medical anatomy detection
The state-of-the-art object detection algorithm learns a binary classifier to differentiate the foreground object from the background. Since the detection algorithm exhaustively s...
Shaohua Kevin Zhou, Jinghao Zhou, Dorin Comaniciu
AAAI
2007
13 years 10 months ago
Multi-Label Learning by Instance Differentiation
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...
Min-Ling Zhang, Zhi-Hua Zhou
CVPR
2008
IEEE
14 years 9 months ago
Face alignment via boosted ranking model
Face alignment seeks to deform a face model to match it with the features of the image of a face by optimizing an appropriate cost function. We propose a new face model that is al...
Gianfranco Doretto, Hao Wu, Xiaoming Liu 0002