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» Active learning in very large databases
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AAAI
2010
15 years 1 months ago
G-Optimal Design with Laplacian Regularization
In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attem...
Chun Chen, Zhengguang Chen, Jiajun Bu, Can Wang, L...
ICASSP
2008
IEEE
15 years 6 months ago
Graph laplacian for interactive image retrieval
Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in a step-by-step lab...
Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert...
AUSAI
2003
Springer
15 years 5 months ago
Efficiently Mining Frequent Patterns from Dense Datasets Using a Cluster of Computers
Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we pr...
Yudho Giri Sucahyo, Raj P. Gopalan, Amit Rudra
KDD
2008
ACM
150views Data Mining» more  KDD 2008»
16 years 3 days ago
Hypergraph spectral learning for multi-label classification
A hypergraph is a generalization of the traditional graph in which the edges are arbitrary non-empty subsets of the vertex set. It has been applied successfully to capture highord...
Liang Sun, Shuiwang Ji, Jieping Ye
BMCBI
2010
193views more  BMCBI 2010»
14 years 6 months ago
Mayday - integrative analytics for expression data
Background: DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the ge...
Florian Battke, Stephan Symons, Kay Nieselt