421 search results - page 6 / 85 » Reducing Label Complexity by Learning From Bags |
124
Voted
AAAI
2007
15 years 1 months ago
2007
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...
ICASSP
2010
IEEE
14 years 11 months ago
2010
IEEE
The goal of semi-supervised image segmentation is to obtain the segmentation from a partially labeled image. By utilizing the image manifold structure in labeled and unlabeled pix...
130
Voted
CVPR
2012
IEEE
13 years 2 months ago
2012
IEEE
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
139
click to vote
KDD
2012
ACM
13 years 2 months ago
2012
ACM
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
ICML
2010
IEEE
15 years 21 days ago
2010
IEEE
We propose a max-margin formulation for the multi-label classification problem where the goal is to tag a data point with a set of pre-specified labels. Given a set of L labels, a...