Titlebook: Inductive Logic Programming; 17th International C Hendrik Blockeel,Jan Ramon,Prasad Tadepalli Conference proceedings 2008 Springer-Verlag B

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Beyond Prediction: Directions for Probabilistic and Relational Learning can address. Recent work has extended these boundaries even further by unifying these two powerful learning frameworks. However, new frontiers await. Current techniques are capable of learning only a subset of the knowledge needed by practitioners in important domains, and further unification of pr

BRAND

Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract)data sets used by most PILP systems and applications have non-probabilistic class values, like those used in ILP systems. The main reason for this is the lack of an obvious source of probabilistic class values. In this context, we investigate the use of Abductive Stochastic Logic Programs (SLPs) for

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柱廊

Learning to Assign Degrees of Belief in Relational Domainsly based on our judgements of the probability of uncertain events such as success in a new medical treatment or the state of the market. For example, if an agent wishes to employ the expected-utility paradigm of decision theory in order to guide its actions, it must assign subjective probabilities t

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FISC

Clustering Relational Data Based on Randomized Propositionalizationh based on randomized propositionalization, which allows for applying standard clustering algorithms like KMeans to multi-relational data. We describe how random rules are generated and then turned into boolean-valued features. Clustering generally is not straightforward to evaluate, but preliminary

Obstreperous

Structural Statistical Software Testing with Active Learning in a Graph exploits the . in the control flow graph, that is, paths which are actually exerted for some values of the program input; the limitation is that feasible paths are massively outnumbered by infeasible ones. Addressing this limitation, this paper presents an active learning algorithm aimed at samplin

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