Titlebook: Inductive Logic Programming; 22nd International C Fabrizio Riguzzi,Filip ?elezny Conference proceedings 2013 Springer-Verlag Berlin Heidelb

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recede

Pairwise Markov Logic, inference methods can be employed for pairwise MLNs without the overhead of devising or implementing high-order variants. Experiments on two relational datasets confirm the usefulness of this reduction approach.

Charlatan

,Identifying Driver’s Cognitive Load Using Inductive Logic Programming,le for rule verification and are actively employed for user-oriented interface design. Realistic experiments were conducted to demonstrate the learning performance of this approach. Reasonable accuracy was achieved for an appropriate service providing safe driving.

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conference

Conference proceedings 2013Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inferenc

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A Relational Approach to Tool-Use Learning in Robots,rates informative experiments while containing the search space to a practical number of experiments. Relational learning generalises across objects and tasks to learn the spatial and structural constraints that describe useful tools and how they should be employed. The system is evaluated in a simulated robot environment.

恃強凌弱的人

Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns,whether or not a given ordered graph is contained in the ordered graph language for a given ordered graph pattern. We also implement the proposed algorithm on a computer and evaluate the algorithm by reporting and discussing experimental results.

mechanism

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Itemset-Based Variable Construction in Multi-relational Supervised Learning,in a parameter-free criterion to assess the relevance of the constructed variables. A greedy algorithm is then proposed in order to explore the space of the considered itemsets. Experiments on multi-relationalal datasets confirm the advantage of the approach.

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