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159
Voted
FUZZIEEE
2007
IEEE
15 years 11 months ago
2007
IEEE
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...
133
Voted
AI
2001
Springer
15 years 9 months ago
2001
Springer
Abstract. This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) – primarily unrestricted Bayesian networks and Bayesian m...
128
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UAI
1998
15 years 6 months ago
1998
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
138
Voted
GFKL
2005
Springer
15 years 10 months ago
2005
Springer
Abstract. We consider the problem of finding communities in large linked networks such as web structures or citation networks. We review similarity measures for linked objects and...
153
Voted
ICML
2010
IEEE
15 years 6 months ago
2010
IEEE
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...