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    ICMLA
    2009
    167views Machine Learning» more  ICMLA 2009»
    Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
    13 years 2 months ago
    Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
    Download  ftp.cs.wisc.edu
    Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
    Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
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    JMLR
    2006
    138views more  JMLR 2006»
    Noisy-OR Component Analysis and its Application to Link Analysis
    13 years 4 months ago
    Noisy-OR Component Analysis and its Application to Link Analysis
    Download  www.cs.pitt.edu
    We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
    Tomás Singliar, Milos Hauskrecht
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    ICML
    2005
    IEEE
    112views Machine Learning» more  ICML 2005»
    Learning first-order probabilistic models with combining rules
    14 years 5 months ago
    Learning first-order probabilistic models with combining rules
    Download  www.biostat.wisc.edu
    Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
    Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
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    FUIN
    2008
    108views more  FUIN 2008»
    Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
    13 years 2 months ago
    Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
    Download  lirias.kuleuven.be
    Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such rel...
    Wannes Meert, Jan Struyf, Hendrik Blockeel
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    ICML
    2007
    IEEE
    125views Machine Learning» more  ICML 2007»
    Parameter learning for relational Bayesian networks
    14 years 5 months ago
    Parameter learning for relational Bayesian networks
    Download  www.machinelearning.org
    We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
    Manfred Jaeger
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