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» Visualization of Labeled Data Using Linear Transformations
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ICASSP
2011
IEEE
12 years 10 months ago
Semi-supervised handwritten digit recognition using very few labeled data
We propose a novel semi-supervised classifier for handwritten digit recognition problems that is based on the assumption that any digit can be obtained as a slight transformation...
Steven Van Vaerenbergh, Ignacio Santamaría,...
CVPR
2006
IEEE
14 years 8 months ago
Semi-Supervised Classification Using Linear Neighborhood Propagation
We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...
CIDM
2007
IEEE
14 years 17 days ago
Privacy Preserving Burst Detection of Distributed Time Series Data Using Linear Transforms
— In this paper, we consider burst detection within the context of privacy. In our scenario, multiple parties want to detect a burst in aggregated time series data, but none of t...
Lisa Singh, Mehmet Sayal
WSDM
2009
ACM
191views Data Mining» more  WSDM 2009»
14 years 1 months ago
Generating labels from clicks
The ranking function used by search engines to order results is learned from labeled training data. Each training point is a (query, URL) pair that is labeled by a human judge who...
Rakesh Agrawal, Alan Halverson, Krishnaram Kenthap...
CVPR
2008
IEEE
14 years 8 months ago
Semi-supervised boosting using visual similarity learning
The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semisupervise...
Christian Leistner, Helmut Grabner, Horst Bischof