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» Self-taught learning: transfer learning from unlabeled data
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ICML
2006
IEEE
16 years 2 months ago
Deterministic annealing for semi-supervised kernel machines
An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For marginbased...
Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chape...
AIRS
2010
Springer
14 years 12 months ago
Semantic Relation Extraction Based on Semi-supervised Learning
Many tasks of information extraction or natural language processing have a property that the data naturally consist of several views--disjoint subsets of features. Specifically, a ...
Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka
126
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CVPR
2009
IEEE
16 years 9 months ago
An Empirical Bayes Approach to Contextual Region Classification
This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent developments in information-theoretic denoising. The chief novelty of ...
Svetlana Lazebnik (UNC Chapel Hill), Maxim Raginsk...
ICML
2004
IEEE
16 years 2 months ago
Co-EM support vector learning
Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Co-EM outperforms ...
Ulf Brefeld, Tobias Scheffer
ICASSP
2010
IEEE
15 years 2 months ago
Fast semi-supervised image segmentation by novelty selection
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...
António R. C. Paiva, Tolga Tasdizen