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» Predicting labels for dyadic data
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AAAI
2008
14 years 6 days ago
Semi-supervised Classification Using Local and Global Regularization
In this paper, we propose a semi-supervised learning (SSL) algorithm based on local and global regularization. In the local regularization part, our algorithm constructs a regular...
Fei Wang, Tao Li, Gang Wang, Changshui Zhang
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
14 years 10 months ago
Extracting shared subspace for multi-label classification
Multi-label problems arise in various domains such as multitopic document categorization and protein function prediction. One natural way to deal with such problems is to construc...
Shuiwang Ji, Lei Tang, Shipeng Yu, Jieping Ye
CVPR
2012
IEEE
12 years 9 days ago
Weakly supervised structured output learning for semantic segmentation
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
SDM
2004
SIAM
187views Data Mining» more  SDM 2004»
13 years 11 months ago
Class-Specific Ensembles for Active Learning
In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is...
Amit Mandvikar, Huan Liu
ICML
2009
IEEE
14 years 10 months ago
Multi-class image segmentation using conditional random fields and global classification
A key aspect of semantic image segmentation is to integrate local and global features for the prediction of local segment labels. We present an approach to multi-class segmentatio...
Nils Plath, Marc Toussaint, Shinichi Nakajima