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» Weighted Substructure Mining for Image Analysis
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MLG
2007
Springer
13 years 10 months ago
Weighted Substructure Mining for Image Analysis
1 In web-related applications of image categorization, it is desirable to derive an interpretable classification rule with high accuracy. Using the bag-of-words representation and...
Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Taku K...
CAIP
2009
Springer
155views Image Analysis» more  CAIP 2009»
13 years 11 months ago
Algorithms for the Sample Mean of Graphs
Measures of central tendency for graphs are important for protoype construction, frequent substructure mining, and multiple alignment of protein structures. This contribution propo...
Brijnesh J. Jain, Klaus Obermayer
ICDM
2010
IEEE
208views Data Mining» more  ICDM 2010»
13 years 2 months ago
Bonsai: Growing Interesting Small Trees
Graphs are increasingly used to model a variety of loosely structured data such as biological or social networks and entityrelationships. Given this profusion of large-scale graph ...
Stephan Seufert, Srikanta J. Bedathur, Juliá...
DAWAK
2010
Springer
13 years 4 months ago
Region of Interest Based Image Categorization
Region Of Interest Based Image Classification (ROIBIC) is a mechanism for categorising images according to some specific component or object that features across a given image set....
Ashraf Elsayed, Frans Coenen, Marta García-...
BMCBI
2008
144views more  BMCBI 2008»
13 years 4 months ago
WGCNA: an R package for weighted correlation network analysis
Background: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method ...
Peter Langfelder, Steve Horvath