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» An Application of Boosting to Graph Classification
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KDD
2009
ACM
163views Data Mining» more  KDD 2009»
14 years 8 months ago
Large-scale graph mining using backbone refinement classes
We present a new approach to large-scale graph mining based on so-called backbone refinement classes. The method efficiently mines tree-shaped subgraph descriptors under minimum f...
Andreas Maunz, Christoph Helma, Stefan Kramer
AIPS
2008
13 years 9 months ago
Learning Relational Decision Trees for Guiding Heuristic Planning
The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computat...
Tomás de la Rosa, Sergio Jiménez, Da...
ICML
2004
IEEE
14 years 8 months ago
Semi-supervised learning using randomized mincuts
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...
DATAMINE
2010
161views more  DATAMINE 2010»
13 years 4 months ago
Predicting labels for dyadic data
: In dyadic prediction, the input consists of a pair of items (a dyad), and the goal is to predict the value of an observation related to the dyad. Special cases of dyadic predicti...
Aditya Krishna Menon, Charles Elkan
ECCV
2002
Springer
14 years 9 months ago
Transitions of the 3D Medial Axis under a One-Parameter Family of Deformations
The instabilities of the medial axis of a shape under deformations have long been recognized as a major obstacle to its use in recognition and other applications. These instabilit...
Peter J. Giblin, Benjamin B. Kimia