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KDD
2009
ACM
179views Data Mining» more  KDD 2009»
14 years 3 months ago
Identifying graphs from noisy and incomplete data
There is a growing wealth of data describing networks of various types, including social networks, physical networks such as transportation or communication networks, and biologic...
Galileo Mark S. Namata Jr., Lise Getoor
ICML
2004
IEEE
14 years 9 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...
ICFCA
2010
Springer
14 years 1 months ago
A Survey of Hybrid Representations of Concept Lattices in Conceptual Knowledge Processing
A feature of Formal Concept Analysis is the use of the line diagram of the concept lattice to visualize a conceptual space. The line diagram is a specialized form of Hasse diagram ...
Peter W. Eklund, Jean Villerd
APVIS
2008
13 years 10 months ago
ZAME: Interactive Large-Scale Graph Visualization
We present the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix...
Niklas Elmqvist, Thanh-Nghi Do, Howard Goodell, Na...
NIPS
2000
13 years 10 months ago
Structure Learning in Human Causal Induction
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
Joshua B. Tenenbaum, Thomas L. Griffiths