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UAI
2003
13 years 9 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
JMLR
2006
118views more  JMLR 2006»
13 years 7 months ago
Learning Factor Graphs in Polynomial Time and Sample Complexity
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...
Pieter Abbeel, Daphne Koller, Andrew Y. Ng
VLDB
1998
ACM
147views Database» more  VLDB 1998»
13 years 12 months ago
Scalable Techniques for Mining Causal Structures
Mining for association rules in market basket data has proved a fruitful areaof research. Measures such as conditional probability (confidence) and correlation have been used to i...
Craig Silverstein, Sergey Brin, Rajeev Motwani, Je...
SDM
2012
SIAM
282views Data Mining» more  SDM 2012»
11 years 10 months ago
Citation Prediction in Heterogeneous Bibliographic Networks
To reveal information hiding in link space of bibliographical networks, link analysis has been studied from different perspectives in recent years. In this paper, we address a no...
Xiao Yu, Quanquan Gu, Mianwei Zhou, Jiawei Han
CONEXT
2010
ACM
13 years 5 months ago
The Internet is flat: modeling the transition from a transit hierarchy to a peering mesh
Recent measurements and anecdotal evidence indicate that the Internet ecosystem is rapidly evolving from a multi-tier hierarchy built mostly with transit (customer-provider) links...
Amogh Dhamdhere, Constantine Dovrolis