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    JMLR
    2010
    149views more  JMLR 2010»
    Learning Bayesian Network Structure using LP Relaxations
    12 years 11 months ago
    Learning Bayesian Network Structure using LP Relaxations
    Download  people.csail.mit.edu
    We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference pr...
    Tommi Jaakkola, David Sontag, Amir Globerson, Mari...
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    ML
    2006
    ACM
    142views Machine Learning» more  ML 2006»
    The max-min hill-climbing Bayesian network structure learning algorithm
    13 years 4 months ago
    The max-min hill-climbing Bayesian network structure learning algorithm
    Download  www.dsl-lab.org
    We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
    Ioannis Tsamardinos, Laura E. Brown, Constantin F....
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    IJAR
    2006
    89views more  IJAR 2006»
    Learning probabilistic decision graphs
    13 years 4 months ago
    Learning probabilistic decision graphs
    Download  www.cs.aau.dk
    Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
    Manfred Jaeger, Jens D. Nielsen, Tomi Silander
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    UAI
    1996
    146views Artificial Intelligence» more  UAI 1996»
    Context-Specific Independence in Bayesian Networks
    13 years 5 months ago
    Context-Specific Independence in Bayesian Networks
    Download  robotics.stanford.edu
    Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of th...
    Craig Boutilier, Nir Friedman, Moisés Golds...
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    IJCAI
    2007
    215views Artificial Intelligence» more  IJCAI 2007»
    A Theoretical Framework for Learning Bayesian Networks with Parameter Inequality Constraints
    13 years 6 months ago
    A Theoretical Framework for Learning Bayesian Networks with Parameter Inequality Constraints
    Download  www.ijcai.org
    The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
    Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
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    ICDM
    2003
    IEEE
    104views Data Mining» more  ICDM 2003»
    Structure Search and Stability Enhancement of Bayesian Networks
    13 years 9 months ago
    Structure Search and Stability Enhancement of Bayesian Networks
    Download  crd.lbl.gov
    Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
    Hanchuan Peng, Chris H. Q. Ding
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    GECCO
    2007
    Springer
    558views Optimization» more  GECCO 2007»
    A chain-model genetic algorithm for Bayesian network structure learning
    13 years 10 months ago
    A chain-model genetic algorithm for Bayesian network structure learning
    Download  www.cs.bham.ac.uk
    Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
    Ratiba Kabli, Frank Herrmann, John McCall
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    KDD
    2004
    ACM
    138views Data Mining» more  KDD 2004»
    Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
    14 years 5 months ago
    Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
    Download  www.gwu.edu
    Rebecca N. Wright, Zhiqiang Yang
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    ICML
    1996
    IEEE
    159views Machine Learning» more  ICML 1996»
    Discretizing Continuous Attributes While Learning Bayesian Networks
    14 years 5 months ago
    Discretizing Continuous Attributes While Learning Bayesian Networks
    Download  www.cs.huji.ac.il
    We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
    Moisés Goldszmidt, Nir Friedman
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    ICML
    2009
    IEEE
    163views Machine Learning» more  ICML 2009»
    Structure learning of Bayesian networks using constraints
    14 years 5 months ago
    Structure learning of Bayesian networks using constraints
    Download  www.ecse.rpi.edu
    This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
    Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
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