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» Random Spanning Trees and the Prediction of Weighted Graphs
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JMLR
2010
179views more  JMLR 2010»
13 years 1 months ago
PAC-Bayesian Analysis of Co-clustering and Beyond
We derive PAC-Bayesian generalization bounds for supervised and unsupervised learning models based on clustering, such as co-clustering, matrix tri-factorization, graphical models...
Yevgeny Seldin, Naftali Tishby
CORR
2011
Springer
151views Education» more  CORR 2011»
13 years 2 months ago
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject’s behavior during a scanning se...
Vincent Michel, Alexandre Gramfort, Gaël Varo...
WEA
2007
Springer
74views Algorithms» more  WEA 2007»
14 years 1 months ago
Landmark-Based Routing in Dynamic Graphs
Many speed-up techniques for route planning in static graphs exist, only few of them are proven to work in a dynamic scenario. Most of them use preprocessed information, which has ...
Daniel Delling, Dorothea Wagner
BMCBI
2010
178views more  BMCBI 2010»
13 years 7 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
AAAI
2011
12 years 7 months ago
Exploiting Phase Transition in Latent Networks for Clustering
In this paper, we model the pair-wise similarities of a set of documents as a weighted network with a single cutoff parameter. Such a network can be thought of an ensemble of unwe...
Vahed Qazvinian, Dragomir R. Radev