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    IJAR
    2010
    152views more  IJAR 2010»
    Structural-EM for learning PDG models from incomplete data
    13 years 3 months ago
    Structural-EM for learning PDG models from incomplete data
    Download  pgm08.cs.aau.dk
    Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by...
    Jens D. Nielsen, Rafael Rumí, Antonio Salme...
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    ML
    2008
    ACM
    150views Machine Learning» more  ML 2008»
    Learning probabilistic logic models from probabilistic examples
    13 years 5 months ago
    Learning probabilistic logic models from probabilistic examples
    Download  ftp.cs.wisc.edu
    Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
    Jianzhong Chen, Stephen Muggleton, José Car...
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    IAT
    2008
    IEEE
    103views Intelligent Agents» more  IAT 2008»
    Structured Learning of Component Dependencies in AmI Systems
    13 years 5 months ago
    Structured Learning of Component Dependencies in AmI Systems
    Download  fsstud.uni-duisburg.de
    As information and communication technologies are becoming an integral part of our homes, the demand for AmI systems with assistive functionality is increasing. A great effort has...
    Todor Dimitrov, Josef Pauli, Edwin Naroska, Christ...
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    ICMLA
    2009
    169views Machine Learning» more  ICMLA 2009»
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    13 years 3 months ago
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    Download  www.ipb.uni-bonn.de
    Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
    Johannes Hartz
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    JMLR
    2010
    95views more  JMLR 2010»
    Feature Extraction for Machine Learning: Logic-Probabilistic Approach
    13 years 3 days ago
    Feature Extraction for Machine Learning: Logic-Probabilistic Approach
    Download  featureselection.asu.edu
    The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly s...
    Vladimir Gorodetsky, Vladimir Samoilov
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