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» Learning Causal Structure from Overlapping Variable Sets
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CVPR
2008
IEEE
14 years 9 months ago
Spectral methods for semi-supervised manifold learning
Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data poin...
Zhenyue Zhang, Hongyuan Zha, Min Zhang
PPSN
2004
Springer
14 years 1 months ago
A Primer on the Evolution of Equivalence Classes of Bayesian-Network Structures
Bayesian networks (BN) constitute a useful tool to model the joint distribution of a set of random variables of interest. To deal with the problem of learning sensible BN models fr...
Jorge Muruzábal, Carlos Cotta
ICASSP
2011
IEEE
12 years 11 months ago
Variability regularization in large-margin classification
This paper introduces a novel regularization strategy to address the generalization issues for large-margin classifiers from the Empirical Risk Minimization (ERM) perspective. Fi...
Dwi Sianto Mansjur, Ted S. Wada, Biing-Hwang Juang
UAI
2003
13 years 9 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
WWW
2009
ACM
14 years 8 months ago
Enhancing diversity, coverage and balance for summarization through structure learning
Document summarization plays an increasingly important role with the exponential growth of documents on the Web. Many supervised and unsupervised approaches have been proposed to ...
Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, Y...