[PDF] Top 20 Learning probabilistic relational models with (partially structured) graph databases
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Learning probabilistic relational models with (partially structured) graph databases
... Abstract—Probabilistic Relational Models (PRMs) such as Di- rected Acyclic Probabilistic Entity Relationship (DAPER) models are probabilistic models dealing with ... Voir le document complet
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Probabilistic relational models learning from graph databases
... deal with partially structured ...structured graph databases using MLN ...the probabilistic dependencies using an MLN learning algorithm is ...of probabilistic dependencies ... Voir le document complet
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DAPER joint learning from partially structured Graph Databases
... joint learning from partially structured graph databases, where we want to learn at the same time the ER schema and the probabilistic ...of probabilistic dependencies of the ... Voir le document complet
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Improving uncertain reasoning combining probabilistic relational models and expert knowledge
... on learning logical rules directly from knowledge bases, Some works have already focused on learning what they define as explanation trees (similar to decision trees) from Bayesian networks (Flores, 2005, ... Voir le document complet
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Probabilistic Relational Models for Customer Preference Modelling and Recommendation
... 2.3 Probabilistic Relational Model Bayesian networks have been one of the main models for reasoning under ...using relational represen- tation. Converting relational data into flat data ... Voir le document complet
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An exact approach to learning probabilistic relational model
... 3. Relational BFHS: A New Approach to Learning Probabilistic Relational Models The BFHS search described in section ...complete relational dataset. We call it Relational ... Voir le document complet
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Probabilistic relational model benchmark generation: Principle and application
... Random probabilistic relational models generation has to be es- tablished in order to evaluate proposed learning approaches in a common ...generated relational synthetic data to perform ... Voir le document complet
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A Framework for Offline Evaluation of Recommender Systems based on Probabilistic Relational Models
... deal with single-table data whereas recent studies on recom- mender systems focus on the use of relational (multi-table, multi-entity) ...works with relational data, and is based on ... Voir le document complet
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Integrating spatial information into probabilistic relational model
... to learning the ...autocorrelation with the introduction of aggregated attributes on partition classes and a special constraint on the orientation of edges to avoid ...directly with the spatial ... Voir le document complet
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Handling uncertainty in relational databases with possibility theory - A survey of different modelings
... bilistic models, with different levels of expressiveness, but also dedicated to dif- ferent database tasks (design, data cleaning, ...compromise) with probabilistic approaches; (ii) the com- ... Voir le document complet
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Privacy leakage in multi-relational learning via unwanted classification models
... benchmarking databases for multi-relational classification were ...other relational learning methods that may be used to evaluate the effectiveness of the subschemas selected by the PPMC ...a ... Voir le document complet
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Relational Reinforcement Learning for Planning with Exogenous Effects
... Abstract Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive ...expressive models that planners do, which forces complex ... Voir le document complet
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Probabilistic relational models: learning and evaluation
... dimensions, with several different types of ...from relational data representation. Statistical relational learning (SRL) is an emerging area of machine learning that enable effective ... Voir le document complet
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Using Probabilistic Relational Models to Generate Synthetic Spatial or Non-spatial Databases
... synthetic relational data that resemble real world data, we should consider dependencies among attributes or those among ...uses probabilistic models to describe how data is ...generative ... Voir le document complet
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Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
... identified graph over the iterations of the algorithm, with n = 2000, δ = 2, and maximum degree β = ...the graph is correct. After the third, the sparsity of the graph has not changed and the ... Voir le document complet
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A hybrid approach for probabilistic relational models structure learning
... structure learning. is known as an NP-Hard problem [3]. BN structure learning methods are divided into three main ...structure with this information. The second family treats structure ... Voir le document complet
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Qualitative probabilistic relational models
... manually, with the help of experts [13]. Automatically learning a Bayesian network typically requires a large amount of sufficiently rich data, which may prove prohibitive for various real-world ...Since ... Voir le document complet
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PAutomaC: a probabilistic automata and hidden Markov models learning competition
... automata learning meth- ods, PAutomaC was designed in such a way that it provided directions to future theoretical work and algorithm ...tomata learning competitions (see Section ...fixed: learning ... Voir le document complet
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Provenance and Probabilities in Relational Databases: From Theory to Practice
... Exploiting the query structure. Jha and Suciu [33] have shown that, when queries have spe- cific forms, it is possible to construct Boolean provenance circuits of certain types, that allow for efficient probability ... Voir le document complet
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Preserving object-relational databases for the next generations
... Along with a saved bitstream of the software application required to display and interact with the document, and the saved database itself, the emulator runs on a virtual machine, hence recreating the ... Voir le document complet
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