the proceedings contain 14 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Reframing on relational data;inductive learning using constraint-driven bias;nonmonotonic ...
ISBN:
(纸本)9783319237077
the proceedings contain 14 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Reframing on relational data;inductive learning using constraint-driven bias;nonmonotonic learning in large biological networks;construction of complex aggregates with random restart hill-climbing;logical minimisation of meta-rules within meta-interpretive learning;goal and plan recognition via parse trees using prefix and infix probability computation;effectively creating weakly labeled training examples via approximate domain knowledge;learning prime implicant conditions from interpretation transition;statistical relational learning for handwriting recognition;the most probable explanation for probabilistic logic programs with annotated disjunctions;towards machine learning of predictive models from ecological data;pagerank, proPPR, and stochastic logic programs;complex aggregates over clusters of elements and on the complexity of frequent subtree mining in very simple structures.
this book constitutes the thoroughly refereed post-conference proceedings of the 24th international conference on inductive logic programming, ilp 2014, held in Nancy, France, in September 2014. the 14 revised papers ...
ISBN:
(纸本)9783319237077
this book constitutes the thoroughly refereed post-conference proceedings of the 24th international conference on inductive logic programming, ilp 2014, held in Nancy, France, in September 2014. the 14 revised papers presented were carefully reviewed and selected from 41 submissions. the papers focus on topics such as the inducing of logic programs, learning from data represented withlogic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.
the proceedings contain 24 papers from the inductivelogicprogramming - 13thinternationalconference, ilp 2003. the topics discussed include: complexity parameters for first-order classes;applying theory revision to...
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the proceedings contain 24 papers from the inductivelogicprogramming - 13thinternationalconference, ilp 2003. the topics discussed include: complexity parameters for first-order classes;applying theory revision to the design of distributed databases;ilp for mathematical discovery;efficient data structures for inductivelogicprogramming;graph kernels and gaussian processes for relational reinforcement learning and on condensation of a clause.
the proceedings contain 24 papers. the topics discussed include: knowledge-directed theory revision;towards clausal discovery for stream mining;on the relationship between logical Bayesian networks and probabilistic l...
ISBN:
(纸本)364213839X
the proceedings contain 24 papers. the topics discussed include: knowledge-directed theory revision;towards clausal discovery for stream mining;on the relationship between logical Bayesian networks and probabilistic logicprogramming based on the distribution semantics;induction of relational algebra expressions;a logic-based approach to relation extraction from texts;discovering rules by meta-level abduction;inductive generalization of analytically learned goal hierarchies;nonmonotonic onto-relational learning;cp-logictheory inference with contextual variable elimination and comparison to BDD based inference methods;speeding up inference in statistical relational learning by clustering similar query literals;an inductivelogicprogramming approach to validate hexose binding biochemical knowledge;boosting first-order clauses for large, skewed data sets;and transfer learning via relational templates.
We revisit an application developed originally using abductive inductivelogicprogramming (ilp) for modeling inhibition in metabolic networks. the example data was derived from studies of the effects of toxins on rat...
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We revisit an application developed originally using abductive inductivelogicprogramming (ilp) for modeling inhibition in metabolic networks. the example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ilp (Pilp) approaches-abductive Stochastic logic Programs (SLPs) and programming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a Pilp framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ilp, the Pilp approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the Pilp approach provides a way of learning probabilistic logic models from probabilistic examples, and the Pilp models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared withthe Pilp models learned from non-probabilistic examples.
the field of inductivelogicprogramming (ilp) has made steady progress, since the first ilp workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has be...
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the field of inductivelogicprogramming (ilp) has made steady progress, since the first ilp workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ilp and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. the goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.
Stochastically searching the space of candidate clauses is an appealing way to scale up ilp to large datasets. We address an approach that uses a Bayesian network model to adaptively guide search in this space. We exa...
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ISBN:
(纸本)9783540784685
Stochastically searching the space of candidate clauses is an appealing way to scale up ilp to large datasets. We address an approach that uses a Bayesian network model to adaptively guide search in this space. We examine guiding search towards areas that previously performed well and towards areas that ilp has not yet thoroughly explored. We show improvement in area under the curve for recall-precision curves using these modifications.
A key feature of ProPPR, a recent probabilistic logic language inspired by stochastic logic programs (SLPs), is its use of personalized PageRank for efficient inference. We adopt this view of probabilistic inference a...
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ISBN:
(数字)9783319237084
ISBN:
(纸本)9783319237084;9783319237077
A key feature of ProPPR, a recent probabilistic logic language inspired by stochastic logic programs (SLPs), is its use of personalized PageRank for efficient inference. We adopt this view of probabilistic inference as a random walk over a graph constructed from a labeled logic program to investigate the relationship between these two languages, showing that the differences in semantics rule out direct, generally applicable translations between them.
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